Lineage expression over time
#Set working directory to appropriate folder for inputs and outputs on Google Drive
#Initialize
Load data
rm(list = ls())
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(Seurat)Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObject
Attaching splibrary(ggplot2)
library(RColorBrewer)
library(ggpubr)
`%nin%` = Negate(`%in%`)#Plot cell cycle
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')
Markers within each first drug object to find subgroups (ie analogous to NGFR/EGFR)
need to make Idents metadata object which says if the cells are included in the combined lins list, or if they were filtered
#load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/all_data_markers.RData')
cis_markers <- FindAllMarkers(cis, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 1
| | 0 % ~calculating
|++ | 3 % ~00s
|++++ | 7 % ~00s
|++++++ | 10% ~00s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~04s
|++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 6 % ~06s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 3 % ~03s
|++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 7 % ~02s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~04s
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|++ | 3 % ~04s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~07s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~03s
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|++ | 3 % ~03s
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|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 7 % ~05s
|+++++ | 8 % ~05s
|+++++ | 9 % ~04s
|++++++ | 11% ~04s
|++++++ | 12% ~04s
|+++++++ | 13% ~04s
|+++++++ | 14% ~04s
|++++++++ | 15% ~04s
|++++++++ | 16% ~04s
|+++++++++ | 17% ~04s
|+++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|++++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|+++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|+++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s cocl2_markers <- FindAllMarkers(cocl2, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~03s
|++ | 3 % ~03s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 9 % ~03s
|+++++ | 10% ~03s
|++++++ | 11% ~03s
|++++++ | 12% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~01s
|++ | 4 % ~01s
|+++ | 5 % ~01s
|++++ | 6 % ~01s
|++++ | 8 % ~01s
|+++++ | 9 % ~01s
|+++++ | 10% ~01s
|++++++ | 11% ~01s
|+++++++ | 12% ~01s
|+++++++ | 14% ~01s
|++++++++ | 15% ~01s
|+++++++++ | 16% ~01s
|+++++++++ | 18% ~01s
|++++++++++ | 19% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~00s
|++++++++++++++++++++++++++++++ | 60% ~00s
|+++++++++++++++++++++++++++++++ | 61% ~00s
|++++++++++++++++++++++++++++++++ | 62% ~00s
|++++++++++++++++++++++++++++++++ | 64% ~00s
|+++++++++++++++++++++++++++++++++ | 65% ~00s
|++++++++++++++++++++++++++++++++++ | 66% ~00s
|++++++++++++++++++++++++++++++++++ | 68% ~00s
|+++++++++++++++++++++++++++++++++++ | 69% ~00s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++ | 71% ~00s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 3 % ~02s
|++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|++++++++ | 14% ~02s
|++++++++ | 16% ~01s
|+++++++++ | 17% ~01s
|++++++++++ | 18% ~01s
|++++++++++ | 20% ~01s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 24% ~01s
|+++++++++++++ | 25% ~01s
|++++++++++++++ | 26% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|++++++++++++++++ | 30% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|++++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~00s
|+++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~04s
|+++ | 5 % ~04s
|++++ | 7 % ~04s
|++++ | 8 % ~04s
|+++++ | 9 % ~04s
|+++++ | 10% ~04s
|++++++ | 11% ~04s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 35% ~03s
|+++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|++++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~03s
|+ | 2 % ~03s
|++ | 3 % ~03s
|++ | 4 % ~03s
|+++ | 5 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|+++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 3 % ~02s
|++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 8 % ~02s
|+++++ | 9 % ~02s
|++++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 27% ~02s
|++++++++++++++ | 28% ~02s
|+++++++++++++++ | 29% ~02s
|++++++++++++++++ | 30% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~02s
|++ | 2 % ~02s
|++ | 3 % ~02s
|+++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|++++++ | 12% ~02s
|+++++++ | 13% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 18% ~02s
|++++++++++ | 19% ~02s
|+++++++++++ | 20% ~02s
|+++++++++++ | 21% ~01s
|++++++++++++ | 22% ~01s
|++++++++++++ | 23% ~01s
|+++++++++++++ | 24% ~01s
|+++++++++++++ | 26% ~01s
|++++++++++++++ | 27% ~01s
|++++++++++++++ | 28% ~01s
|+++++++++++++++ | 29% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++ | 31% ~01s
|++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 34% ~01s
|++++++++++++++++++ | 35% ~01s
|+++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 39% ~01s
|+++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|++++++++++++++++++++++ | 43% ~01s
|++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|+++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|+++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~00s
|++++++++++++++++++++++++++++++++++++++ | 74% ~00s
|++++++++++++++++++++++++++++++++++++++ | 76% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~00s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~11s
|+ | 2 % ~11s
|++ | 3 % ~11s
|++ | 4 % ~11s
|+++ | 5 % ~11s
|+++ | 6 % ~11s
|++++ | 7 % ~11s
|++++ | 8 % ~10s
|+++++ | 9 % ~10s
|+++++ | 10% ~10s
|++++++ | 11% ~10s
|++++++ | 12% ~10s
|+++++++ | 13% ~10s
|+++++++ | 14% ~10s
|++++++++ | 15% ~10s
|++++++++ | 16% ~10s
|+++++++++ | 17% ~10s
|+++++++++ | 18% ~10s
|++++++++++ | 19% ~09s
|++++++++++ | 20% ~09s
|+++++++++++ | 21% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|++++++++++++ | 24% ~09s
|+++++++++++++ | 25% ~09s
|+++++++++++++ | 26% ~09s
|++++++++++++++ | 27% ~08s
|++++++++++++++ | 28% ~08s
|+++++++++++++++ | 29% ~08s
|+++++++++++++++ | 30% ~08s
|++++++++++++++++ | 31% ~08s
|++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|+++++++++++++++++ | 34% ~08s
|++++++++++++++++++ | 35% ~08s
|++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 37% ~07s
|+++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 41% ~07s
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|+++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 47% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 5 % ~04s
|+++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 8 % ~04s
|+++++ | 9 % ~04s
|++++++ | 10% ~04s
|++++++ | 11% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~08s
|+++ | 4 % ~08s
|+++ | 5 % ~07s
|++++ | 6 % ~07s
|++++ | 7 % ~07s
|+++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 10% ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|+++++++ | 13% ~07s
|++++++++ | 14% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 16% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|+++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s dabtram_markers <- FindAllMarkers(dabtram, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~03s
|+++ | 5 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~03s
|++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~02s
|++++++++++++ | 24% ~02s
|+++++++++++++ | 25% ~02s
|++++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|+++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|+++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~01s
|+++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 4 % ~03s
|+++ | 5 % ~03s
|++++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 12% ~03s
|+++++++ | 13% ~03s
|++++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 19% ~03s
|+++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|++++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 26% ~03s
|++++++++++++++ | 27% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|+++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 35% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|+++++++++++++++++++++ | 42% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|+++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~04s
|++ | 2 % ~04s
|++ | 3 % ~04s
|+++ | 5 % ~03s
|+++ | 6 % ~03s
|++++ | 7 % ~03s
|+++++ | 8 % ~03s
|+++++ | 9 % ~03s
|++++++ | 10% ~03s
|++++++ | 11% ~03s
|+++++++ | 13% ~03s
|+++++++ | 14% ~03s
|++++++++ | 15% ~03s
|+++++++++ | 16% ~03s
|+++++++++ | 17% ~03s
|++++++++++ | 18% ~03s
|++++++++++ | 20% ~03s
|+++++++++++ | 21% ~03s
|+++++++++++ | 22% ~03s
|++++++++++++ | 23% ~03s
|+++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 26% ~03s
|++++++++++++++ | 28% ~03s
|+++++++++++++++ | 29% ~03s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~02s
|+++++++++++++++++ | 32% ~02s
|+++++++++++++++++ | 33% ~02s
|++++++++++++++++++ | 34% ~02s
|++++++++++++++++++ | 36% ~02s
|+++++++++++++++++++ | 37% ~02s
|+++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++ | 39% ~02s
|+++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++ | 41% ~02s
|++++++++++++++++++++++ | 43% ~02s
|++++++++++++++++++++++ | 44% ~02s
|+++++++++++++++++++++++ | 45% ~02s
|+++++++++++++++++++++++ | 46% ~02s
|++++++++++++++++++++++++ | 47% ~02s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 59% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 61% ~01s
|++++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 66% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~03s
|++ | 2 % ~02s
|++ | 4 % ~02s
|+++ | 5 % ~02s
|++++ | 6 % ~02s
|++++ | 7 % ~02s
|+++++ | 9 % ~02s
|+++++ | 10% ~02s
|++++++ | 11% ~02s
|+++++++ | 12% ~02s
|+++++++ | 14% ~02s
|++++++++ | 15% ~02s
|+++++++++ | 16% ~02s
|+++++++++ | 17% ~02s
|++++++++++ | 19% ~02s
|++++++++++ | 20% ~02s
|+++++++++++ | 21% ~02s
|++++++++++++ | 22% ~02s
|++++++++++++ | 23% ~02s
|+++++++++++++ | 25% ~02s
|+++++++++++++ | 26% ~02s
|++++++++++++++ | 27% ~02s
|+++++++++++++++ | 28% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++ | 31% ~01s
|+++++++++++++++++ | 32% ~01s
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++ | 36% ~01s
|+++++++++++++++++++ | 37% ~01s
|++++++++++++++++++++ | 38% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++ | 41% ~01s
|+++++++++++++++++++++ | 42% ~01s
|++++++++++++++++++++++ | 43% ~01s
|+++++++++++++++++++++++ | 44% ~01s
|+++++++++++++++++++++++ | 46% ~01s
|++++++++++++++++++++++++ | 47% ~01s
|+++++++++++++++++++++++++ | 48% ~01s
|+++++++++++++++++++++++++ | 49% ~01s
|++++++++++++++++++++++++++ | 51% ~01s
|++++++++++++++++++++++++++ | 52% ~01s
|+++++++++++++++++++++++++++ | 53% ~01s
|++++++++++++++++++++++++++++ | 54% ~01s
|++++++++++++++++++++++++++++ | 56% ~01s
|+++++++++++++++++++++++++++++ | 57% ~01s
|++++++++++++++++++++++++++++++ | 58% ~01s
|++++++++++++++++++++++++++++++ | 59% ~01s
|+++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|++++++++++++++++++++++++++++++++ | 63% ~01s
|+++++++++++++++++++++++++++++++++ | 64% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++ | 68% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~00s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~00s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~08s
|++ | 2 % ~08s
|++ | 3 % ~07s
|+++ | 4 % ~10s
|+++ | 5 % ~09s
|++++ | 6 % ~09s
|++++ | 7 % ~09s
|+++++ | 8 % ~08s
|+++++ | 9 % ~08s
|++++++ | 10% ~08s
|++++++ | 11% ~08s
|+++++++ | 12% ~07s
|+++++++ | 13% ~07s
|++++++++ | 14% ~07s
|++++++++ | 15% ~07s
|+++++++++ | 16% ~07s
|+++++++++ | 17% ~07s
|++++++++++ | 18% ~07s
|++++++++++ | 19% ~07s
|+++++++++++ | 20% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 22% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 24% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~06s
|++++++++++++++ | 27% ~06s
|+++++++++++++++ | 28% ~06s
|+++++++++++++++ | 29% ~06s
|++++++++++++++++ | 30% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 35% ~05s
|+++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 37% ~05s
|++++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 39% ~05s
|+++++++++++++++++++++ | 40% ~05s
|+++++++++++++++++++++ | 41% ~05s
|++++++++++++++++++++++ | 42% ~05s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 46% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|+++++++++++++++++++++++++++ | 54% ~04s
|++++++++++++++++++++++++++++ | 55% ~04s
|++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|+++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 59% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|++++++++++++++++++++++++++++++++ | 64% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~06s
|++ | 2 % ~06s
|++ | 3 % ~06s
|+++ | 4 % ~06s
|+++ | 5 % ~06s
|++++ | 6 % ~06s
|++++ | 7 % ~06s
|+++++ | 8 % ~06s
|+++++ | 9 % ~05s
|++++++ | 10% ~05s
|++++++ | 11% ~05s
|+++++++ | 12% ~05s
|+++++++ | 13% ~05s
|++++++++ | 14% ~05s
|++++++++ | 15% ~05s
|+++++++++ | 16% ~05s
|+++++++++ | 17% ~05s
|++++++++++ | 18% ~05s
|++++++++++ | 19% ~05s
|+++++++++++ | 20% ~05s
|+++++++++++ | 21% ~05s
|++++++++++++ | 22% ~05s
|++++++++++++ | 23% ~05s
|+++++++++++++ | 24% ~05s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|+++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 37% ~04s
|+++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++ | 39% ~04s
|++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 41% ~04s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|+++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 2 % ~10s
|++ | 3 % ~09s
|+++ | 4 % ~09s
|+++ | 5 % ~09s
|++++ | 6 % ~09s
|++++ | 7 % ~09s
|+++++ | 8 % ~09s
|+++++ | 9 % ~09s
|++++++ | 10% ~09s
|++++++ | 11% ~08s
|+++++++ | 12% ~08s
|+++++++ | 13% ~08s
|++++++++ | 14% ~08s
|++++++++ | 15% ~08s
|+++++++++ | 16% ~08s
|+++++++++ | 17% ~08s
|++++++++++ | 18% ~08s
|++++++++++ | 19% ~08s
|+++++++++++ | 20% ~08s
|+++++++++++ | 21% ~07s
|++++++++++++ | 22% ~07s
|++++++++++++ | 23% ~07s
|+++++++++++++ | 24% ~07s
|+++++++++++++ | 25% ~07s
|++++++++++++++ | 26% ~07s
|++++++++++++++ | 27% ~07s
|+++++++++++++++ | 28% ~07s
|+++++++++++++++ | 29% ~08s
|++++++++++++++++ | 30% ~07s
|++++++++++++++++ | 31% ~07s
|+++++++++++++++++ | 32% ~07s
|+++++++++++++++++ | 33% ~07s
|++++++++++++++++++ | 34% ~07s
|++++++++++++++++++ | 35% ~07s
|+++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 37% ~07s
|++++++++++++++++++++ | 38% ~06s
|++++++++++++++++++++ | 39% ~06s
|+++++++++++++++++++++ | 40% ~06s
|+++++++++++++++++++++ | 41% ~06s
|++++++++++++++++++++++ | 42% ~06s
|++++++++++++++++++++++ | 43% ~06s
|+++++++++++++++++++++++ | 44% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|++++++++++++++++++++++++ | 46% ~05s
|++++++++++++++++++++++++ | 47% ~05s
|+++++++++++++++++++++++++ | 48% ~05s
|+++++++++++++++++++++++++ | 49% ~05s
|++++++++++++++++++++++++++ | 51% ~05s
|++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 53% ~05s
|+++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|++++++++++++++++++++++++++++ | 56% ~04s
|+++++++++++++++++++++++++++++ | 57% ~04s
|+++++++++++++++++++++++++++++ | 58% ~04s
|++++++++++++++++++++++++++++++ | 59% ~04s
|++++++++++++++++++++++++++++++ | 60% ~04s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|+++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 63% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|+++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 67% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~03s
|+++++++++++++++++++++++++++++++++++ | 69% ~03s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s all_data.markers <- FindAllMarkers(all_data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~33s
|++ | 2 % ~33s
|++ | 3 % ~32s
|+++ | 4 % ~31s
|+++ | 5 % ~31s
|++++ | 6 % ~29s
|++++ | 7 % ~29s
|+++++ | 9 % ~28s
|+++++ | 10% ~27s
|++++++ | 11% ~27s
|++++++ | 12% ~27s
|+++++++ | 13% ~27s
|+++++++ | 14% ~27s
|++++++++ | 15% ~27s
|++++++++ | 16% ~27s
|+++++++++ | 17% ~26s
|++++++++++ | 18% ~26s
|++++++++++ | 19% ~26s
|+++++++++++ | 20% ~26s
|+++++++++++ | 21% ~25s
|++++++++++++ | 22% ~25s
|++++++++++++ | 23% ~25s
|+++++++++++++ | 24% ~25s
|+++++++++++++ | 26% ~24s
|++++++++++++++ | 27% ~24s
|++++++++++++++ | 28% ~24s
|+++++++++++++++ | 29% ~23s
|+++++++++++++++ | 30% ~23s
|++++++++++++++++ | 31% ~23s
|++++++++++++++++ | 32% ~22s
|+++++++++++++++++ | 33% ~22s
|++++++++++++++++++ | 34% ~22s
|++++++++++++++++++ | 35% ~22s
|+++++++++++++++++++ | 36% ~21s
|+++++++++++++++++++ | 37% ~21s
|++++++++++++++++++++ | 38% ~20s
|++++++++++++++++++++ | 39% ~20s
|+++++++++++++++++++++ | 40% ~20s
|+++++++++++++++++++++ | 41% ~20s
|++++++++++++++++++++++ | 43% ~19s
|++++++++++++++++++++++ | 44% ~19s
|+++++++++++++++++++++++ | 45% ~19s
|+++++++++++++++++++++++ | 46% ~18s
|++++++++++++++++++++++++ | 47% ~18s
|++++++++++++++++++++++++ | 48% ~18s
|+++++++++++++++++++++++++ | 49% ~17s
|+++++++++++++++++++++++++ | 50% ~17s
|++++++++++++++++++++++++++ | 51% ~17s
|+++++++++++++++++++++++++++ | 52% ~16s
|+++++++++++++++++++++++++++ | 53% ~16s
|++++++++++++++++++++++++++++ | 54% ~16s
|++++++++++++++++++++++++++++ | 55% ~16s
|+++++++++++++++++++++++++++++ | 56% ~15s
|+++++++++++++++++++++++++++++ | 57% ~15s
|++++++++++++++++++++++++++++++ | 59% ~14s
|++++++++++++++++++++++++++++++ | 60% ~14s
|+++++++++++++++++++++++++++++++ | 61% ~14s
|+++++++++++++++++++++++++++++++ | 62% ~13s
|++++++++++++++++++++++++++++++++ | 63% ~13s
|++++++++++++++++++++++++++++++++ | 64% ~13s
|+++++++++++++++++++++++++++++++++ | 65% ~12s
|+++++++++++++++++++++++++++++++++ | 66% ~12s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|+++++++++++++++++++++++++++++++++++ | 68% ~11s
|+++++++++++++++++++++++++++++++++++ | 69% ~11s
|++++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~10s
|+++++++++++++++++++++++++++++++++++++ | 72% ~10s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|++++++++++++++++++++++++++++++++++++++ | 74% ~09s
|++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=35s Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~32s
|++ | 2 % ~30s
|++ | 3 % ~31s
|+++ | 4 % ~42s
|+++ | 5 % ~39s
|++++ | 7 % ~36s
|++++ | 8 % ~35s
|+++++ | 9 % ~33s
|+++++ | 10% ~32s
|++++++ | 11% ~31s
|+++++++ | 12% ~31s
|+++++++ | 13% ~30s
|++++++++ | 14% ~29s
|++++++++ | 15% ~29s
|+++++++++ | 16% ~28s
|+++++++++ | 18% ~28s
|++++++++++ | 19% ~27s
|++++++++++ | 20% ~26s
|+++++++++++ | 21% ~26s
|+++++++++++ | 22% ~25s
|++++++++++++ | 23% ~25s
|+++++++++++++ | 24% ~25s
|+++++++++++++ | 25% ~24s
|++++++++++++++ | 26% ~24s
|++++++++++++++ | 27% ~23s
|+++++++++++++++ | 29% ~23s
|+++++++++++++++ | 30% ~23s
|++++++++++++++++ | 31% ~22s
|++++++++++++++++ | 32% ~22s
|+++++++++++++++++ | 33% ~22s
|++++++++++++++++++ | 34% ~21s
|++++++++++++++++++ | 35% ~21s
|+++++++++++++++++++ | 36% ~20s
|+++++++++++++++++++ | 37% ~20s
|++++++++++++++++++++ | 38% ~20s
|++++++++++++++++++++ | 40% ~19s
|+++++++++++++++++++++ | 41% ~19s
|+++++++++++++++++++++ | 42% ~19s
|++++++++++++++++++++++ | 43% ~18s
|++++++++++++++++++++++ | 44% ~18s
|+++++++++++++++++++++++ | 45% ~18s
|++++++++++++++++++++++++ | 46% ~17s
|++++++++++++++++++++++++ | 47% ~17s
|+++++++++++++++++++++++++ | 48% ~17s
|+++++++++++++++++++++++++ | 49% ~16s
|++++++++++++++++++++++++++ | 51% ~16s
|++++++++++++++++++++++++++ | 52% ~16s
|+++++++++++++++++++++++++++ | 53% ~15s
|+++++++++++++++++++++++++++ | 54% ~15s
|++++++++++++++++++++++++++++ | 55% ~15s
|+++++++++++++++++++++++++++++ | 56% ~14s
|+++++++++++++++++++++++++++++ | 57% ~14s
|++++++++++++++++++++++++++++++ | 58% ~13s
|++++++++++++++++++++++++++++++ | 59% ~13s
|+++++++++++++++++++++++++++++++ | 60% ~13s
|+++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~12s
|++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|+++++++++++++++++++++++++++++++++ | 66% ~11s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|+++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~10s
|++++++++++++++++++++++++++++++++++++ | 70% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|+++++++++++++++++++++++++++++++++++++ | 74% ~09s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=33s Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~01m 12s
|++ | 2 % ~01m 11s
|++ | 3 % ~01m 10s
|+++ | 5 % ~01m 09s
|+++ | 6 % ~01m 09s
|++++ | 7 % ~01m 07s
|+++++ | 8 % ~01m 06s
|+++++ | 9 % ~01m 06s
|++++++ | 10% ~01m 05s
|++++++ | 11% ~01m 04s
|+++++++ | 13% ~01m 04s
|+++++++ | 14% ~01m 03s
|++++++++ | 15% ~01m 03s
|+++++++++ | 16% ~01m 02s
|+++++++++ | 17% ~01m 01s
|++++++++++ | 18% ~01m 01s
|++++++++++ | 20% ~60s
|+++++++++++ | 21% ~59s
|+++++++++++ | 22% ~58s
|++++++++++++ | 23% ~57s
|+++++++++++++ | 24% ~58s
|+++++++++++++ | 25% ~57s
|++++++++++++++ | 26% ~56s
|++++++++++++++ | 28% ~55s
|+++++++++++++++ | 29% ~54s
|+++++++++++++++ | 30% ~53s
|++++++++++++++++ | 31% ~52s
|+++++++++++++++++ | 32% ~51s
|+++++++++++++++++ | 33% ~50s
|++++++++++++++++++ | 34% ~49s
|++++++++++++++++++ | 36% ~48s
|+++++++++++++++++++ | 37% ~47s
|+++++++++++++++++++ | 38% ~46s
|++++++++++++++++++++ | 39% ~46s
|+++++++++++++++++++++ | 40% ~45s
|+++++++++++++++++++++ | 41% ~44s
|++++++++++++++++++++++ | 43% ~43s
|++++++++++++++++++++++ | 44% ~42s
|+++++++++++++++++++++++ | 45% ~41s
|+++++++++++++++++++++++ | 46% ~40s
|++++++++++++++++++++++++ | 47% ~40s
|+++++++++++++++++++++++++ | 48% ~39s
|+++++++++++++++++++++++++ | 49% ~38s
|++++++++++++++++++++++++++ | 51% ~37s
|++++++++++++++++++++++++++ | 52% ~37s
|+++++++++++++++++++++++++++ | 53% ~36s
|++++++++++++++++++++++++++++ | 54% ~35s
|++++++++++++++++++++++++++++ | 55% ~34s
|+++++++++++++++++++++++++++++ | 56% ~33s
|+++++++++++++++++++++++++++++ | 57% ~32s
|++++++++++++++++++++++++++++++ | 59% ~31s
|++++++++++++++++++++++++++++++ | 60% ~30s
|+++++++++++++++++++++++++++++++ | 61% ~29s
|++++++++++++++++++++++++++++++++ | 62% ~28s
|++++++++++++++++++++++++++++++++ | 63% ~28s
|+++++++++++++++++++++++++++++++++ | 64% ~27s
|+++++++++++++++++++++++++++++++++ | 66% ~26s
|++++++++++++++++++++++++++++++++++ | 67% ~25s
|++++++++++++++++++++++++++++++++++ | 68% ~24s
|+++++++++++++++++++++++++++++++++++ | 69% ~23s
|++++++++++++++++++++++++++++++++++++ | 70% ~22s
|++++++++++++++++++++++++++++++++++++ | 71% ~22s
|+++++++++++++++++++++++++++++++++++++ | 72% ~21s
|+++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~19s
|++++++++++++++++++++++++++++++++++++++ | 76% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 16sCalculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~01m 07s
|++ | 2 % ~01m 07s
|++ | 3 % ~01m 07s
|+++ | 4 % ~01m 05s
|+++ | 5 % ~01m 04s
|++++ | 6 % ~01m 03s
|++++ | 7 % ~01m 02s
|+++++ | 8 % ~01m 01s
|+++++ | 9 % ~01m 01s
|++++++ | 10% ~01m 01s
|++++++ | 11% ~01m 00s
|+++++++ | 12% ~60s
|+++++++ | 14% ~59s
|++++++++ | 15% ~59s
|++++++++ | 16% ~58s
|+++++++++ | 17% ~58s
|+++++++++ | 18% ~57s
|++++++++++ | 19% ~57s
|++++++++++ | 20% ~56s
|+++++++++++ | 21% ~55s
|+++++++++++ | 22% ~55s
|++++++++++++ | 23% ~54s
|++++++++++++ | 24% ~53s
|+++++++++++++ | 25% ~53s
|++++++++++++++ | 26% ~52s
|++++++++++++++ | 27% ~51s
|+++++++++++++++ | 28% ~52s
|+++++++++++++++ | 29% ~51s
|++++++++++++++++ | 30% ~50s
|++++++++++++++++ | 31% ~49s
|+++++++++++++++++ | 32% ~49s
|+++++++++++++++++ | 33% ~48s
|++++++++++++++++++ | 34% ~47s
|++++++++++++++++++ | 35% ~46s
|+++++++++++++++++++ | 36% ~45s
|+++++++++++++++++++ | 38% ~44s
|++++++++++++++++++++ | 39% ~44s
|++++++++++++++++++++ | 40% ~43s
|+++++++++++++++++++++ | 41% ~42s
|+++++++++++++++++++++ | 42% ~41s
|++++++++++++++++++++++ | 43% ~41s
|++++++++++++++++++++++ | 44% ~40s
|+++++++++++++++++++++++ | 45% ~39s
|+++++++++++++++++++++++ | 46% ~38s
|++++++++++++++++++++++++ | 47% ~38s
|++++++++++++++++++++++++ | 48% ~37s
|+++++++++++++++++++++++++ | 49% ~36s
|+++++++++++++++++++++++++ | 50% ~35s
|++++++++++++++++++++++++++ | 51% ~35s
|+++++++++++++++++++++++++++ | 52% ~34s
|+++++++++++++++++++++++++++ | 53% ~33s
|++++++++++++++++++++++++++++ | 54% ~32s
|++++++++++++++++++++++++++++ | 55% ~32s
|+++++++++++++++++++++++++++++ | 56% ~31s
|+++++++++++++++++++++++++++++ | 57% ~30s
|++++++++++++++++++++++++++++++ | 58% ~30s
|++++++++++++++++++++++++++++++ | 59% ~29s
|+++++++++++++++++++++++++++++++ | 60% ~28s
|+++++++++++++++++++++++++++++++ | 61% ~27s
|++++++++++++++++++++++++++++++++ | 62% ~27s
|++++++++++++++++++++++++++++++++ | 64% ~26s
|+++++++++++++++++++++++++++++++++ | 65% ~25s
|+++++++++++++++++++++++++++++++++ | 66% ~24s
|++++++++++++++++++++++++++++++++++ | 67% ~24s
|++++++++++++++++++++++++++++++++++ | 68% ~23s
|+++++++++++++++++++++++++++++++++++ | 69% ~22s
|+++++++++++++++++++++++++++++++++++ | 70% ~21s
|++++++++++++++++++++++++++++++++++++ | 71% ~21s
|++++++++++++++++++++++++++++++++++++ | 72% ~20s
|+++++++++++++++++++++++++++++++++++++ | 73% ~19s
|+++++++++++++++++++++++++++++++++++++ | 74% ~19s
|++++++++++++++++++++++++++++++++++++++ | 75% ~18s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~17s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~15s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 11sCalculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~01m 50s
|++ | 2 % ~01m 50s
|++ | 3 % ~01m 45s
|+++ | 4 % ~01m 43s
|+++ | 5 % ~01m 40s
|++++ | 6 % ~01m 37s
|++++ | 7 % ~01m 36s
|+++++ | 8 % ~01m 35s
|+++++ | 9 % ~01m 34s
|++++++ | 10% ~01m 33s
|++++++ | 11% ~01m 32s
|+++++++ | 12% ~01m 31s
|+++++++ | 13% ~01m 30s
|++++++++ | 14% ~01m 29s
|++++++++ | 15% ~01m 29s
|+++++++++ | 16% ~01m 28s
|+++++++++ | 17% ~01m 27s
|++++++++++ | 18% ~01m 26s
|++++++++++ | 19% ~01m 27s
|+++++++++++ | 20% ~01m 26s
|+++++++++++ | 21% ~01m 25s
|++++++++++++ | 22% ~01m 24s
|++++++++++++ | 23% ~01m 23s
|+++++++++++++ | 24% ~01m 21s
|+++++++++++++ | 26% ~01m 20s
|++++++++++++++ | 27% ~01m 18s
|++++++++++++++ | 28% ~01m 17s
|+++++++++++++++ | 29% ~01m 16s
|+++++++++++++++ | 30% ~01m 14s
|++++++++++++++++ | 31% ~01m 13s
|++++++++++++++++ | 32% ~01m 12s
|+++++++++++++++++ | 33% ~01m 11s
|+++++++++++++++++ | 34% ~01m 10s
|++++++++++++++++++ | 35% ~01m 09s
|++++++++++++++++++ | 36% ~01m 08s
|+++++++++++++++++++ | 37% ~01m 07s
|+++++++++++++++++++ | 38% ~01m 06s
|++++++++++++++++++++ | 39% ~01m 05s
|++++++++++++++++++++ | 40% ~01m 04s
|+++++++++++++++++++++ | 41% ~01m 03s
|+++++++++++++++++++++ | 42% ~01m 02s
|++++++++++++++++++++++ | 43% ~01m 01s
|++++++++++++++++++++++ | 44% ~60s
|+++++++++++++++++++++++ | 45% ~59s
|+++++++++++++++++++++++ | 46% ~58s
|++++++++++++++++++++++++ | 47% ~56s
|++++++++++++++++++++++++ | 48% ~55s
|+++++++++++++++++++++++++ | 49% ~54s
|+++++++++++++++++++++++++ | 50% ~53s
|++++++++++++++++++++++++++ | 51% ~52s
|+++++++++++++++++++++++++++ | 52% ~51s
|+++++++++++++++++++++++++++ | 53% ~50s
|++++++++++++++++++++++++++++ | 54% ~49s
|++++++++++++++++++++++++++++ | 55% ~47s
|+++++++++++++++++++++++++++++ | 56% ~46s
|+++++++++++++++++++++++++++++ | 57% ~45s
|++++++++++++++++++++++++++++++ | 58% ~44s
|++++++++++++++++++++++++++++++ | 59% ~43s
|+++++++++++++++++++++++++++++++ | 60% ~42s
|+++++++++++++++++++++++++++++++ | 61% ~41s
|++++++++++++++++++++++++++++++++ | 62% ~40s
|++++++++++++++++++++++++++++++++ | 63% ~39s
|+++++++++++++++++++++++++++++++++ | 64% ~38s
|+++++++++++++++++++++++++++++++++ | 65% ~37s
|++++++++++++++++++++++++++++++++++ | 66% ~36s
|++++++++++++++++++++++++++++++++++ | 67% ~35s
|+++++++++++++++++++++++++++++++++++ | 68% ~34s
|+++++++++++++++++++++++++++++++++++ | 69% ~33s
|++++++++++++++++++++++++++++++++++++ | 70% ~31s
|++++++++++++++++++++++++++++++++++++ | 71% ~30s
|+++++++++++++++++++++++++++++++++++++ | 72% ~29s
|+++++++++++++++++++++++++++++++++++++ | 73% ~28s
|++++++++++++++++++++++++++++++++++++++ | 74% ~27s
|++++++++++++++++++++++++++++++++++++++ | 76% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~21s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~19s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 46sCalculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~30s
|++ | 3 % ~30s
|++ | 4 % ~31s
|+++ | 5 % ~30s
|++++ | 7 % ~29s
|++++ | 8 % ~29s
|+++++ | 9 % ~28s
|++++++ | 11% ~40m 03s
|++++++ | 12% ~35m 48s
|+++++++ | 13% ~31m 48s
|++++++++ | 14% ~28m 33s
|++++++++ | 16% ~25m 48s
|+++++++++ | 17% ~23m 29s
|++++++++++ | 18% ~21m 32s
|++++++++++ | 20% ~19m 50s
|+++++++++++ | 21% ~18m 19s
|++++++++++++ | 22% ~16m 59s
|++++++++++++ | 24% ~15m 47s
|+++++++++++++ | 25% ~14m 43s
|++++++++++++++ | 26% ~13m 46s
|++++++++++++++ | 28% ~12m 54s
|+++++++++++++++ | 29% ~12m 06s
|++++++++++++++++ | 30% ~11m 23s
|++++++++++++++++ | 32% ~10m 43s
|+++++++++++++++++ | 33% ~10m 06s
|++++++++++++++++++ | 34% ~09m 33s
|++++++++++++++++++ | 36% ~09m 01s
|+++++++++++++++++++ | 37% ~08m 32s
|++++++++++++++++++++ | 38% ~08m 05s
|++++++++++++++++++++ | 39% ~07m 40s
|+++++++++++++++++++++ | 41% ~07m 16s
|++++++++++++++++++++++ | 42% ~06m 53s
|++++++++++++++++++++++ | 43% ~06m 32s
|+++++++++++++++++++++++ | 45% ~06m 13s
|++++++++++++++++++++++++ | 46% ~05m 54s
|++++++++++++++++++++++++ | 47% ~05m 36s
|+++++++++++++++++++++++++ | 49% ~05m 19s
|+++++++++++++++++++++++++ | 50% ~05m 04s
|++++++++++++++++++++++++++ | 51% ~04m 48s
|+++++++++++++++++++++++++++ | 53% ~04m 34s
|+++++++++++++++++++++++++++ | 54% ~04m 21s
|++++++++++++++++++++++++++++ | 55% ~04m 08s
|+++++++++++++++++++++++++++++ | 57% ~03m 55s
|+++++++++++++++++++++++++++++ | 58% ~03m 43s
|++++++++++++++++++++++++++++++ | 59% ~03m 32s
|+++++++++++++++++++++++++++++++ | 61% ~03m 21s
|+++++++++++++++++++++++++++++++ | 62% ~03m 10s
|++++++++++++++++++++++++++++++++ | 63% ~03m 00s
|+++++++++++++++++++++++++++++++++ | 64% ~02m 50s
|+++++++++++++++++++++++++++++++++ | 66% ~02m 41s
|++++++++++++++++++++++++++++++++++ | 67% ~02m 32s
|+++++++++++++++++++++++++++++++++++ | 68% ~02m 23s
|+++++++++++++++++++++++++++++++++++ | 70% ~02m 15s
|++++++++++++++++++++++++++++++++++++ | 71% ~02m 07s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01m 59s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01m 51s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01m 44s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01m 37s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 30s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 24s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 17s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 11s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 05s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~59s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~53s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~48s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~43s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~37s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~32s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~27s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05m 21sCalculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~01m 07s
|++ | 2 % ~01m 06s
|++ | 4 % ~01m 04s
|+++ | 5 % ~01m 04s
|+++ | 6 % ~01m 03s
|++++ | 7 % ~01m 02s
|+++++ | 8 % ~01m 01s
|+++++ | 10% ~01m 00s
|++++++ | 11% ~59s
|++++++ | 12% ~58s
|+++++++ | 13% ~58s
|++++++++ | 14% ~57s
|++++++++ | 15% ~56s
|+++++++++ | 17% ~55s
|+++++++++ | 18% ~55s
|++++++++++ | 19% ~54s
|+++++++++++ | 20% ~54s
|+++++++++++ | 21% ~53s
|++++++++++++ | 23% ~52s
|++++++++++++ | 24% ~52s
|+++++++++++++ | 25% ~51s
|++++++++++++++ | 26% ~50s
|++++++++++++++ | 27% ~49s
|+++++++++++++++ | 29% ~48s
|+++++++++++++++ | 30% ~48s
|++++++++++++++++ | 31% ~47s
|+++++++++++++++++ | 32% ~46s
|+++++++++++++++++ | 33% ~45s
|++++++++++++++++++ | 35% ~45s
|++++++++++++++++++ | 36% ~44s
|+++++++++++++++++++ | 37% ~43s
|++++++++++++++++++++ | 38% ~42s
|++++++++++++++++++++ | 39% ~41s
|+++++++++++++++++++++ | 40% ~41s
|+++++++++++++++++++++ | 42% ~40s
|++++++++++++++++++++++ | 43% ~39s
|+++++++++++++++++++++++ | 44% ~38s
|+++++++++++++++++++++++ | 45% ~37s
|++++++++++++++++++++++++ | 46% ~37s
|++++++++++++++++++++++++ | 48% ~36s
|+++++++++++++++++++++++++ | 49% ~36s
|+++++++++++++++++++++++++ | 50% ~35s
|++++++++++++++++++++++++++ | 51% ~34s
|+++++++++++++++++++++++++++ | 52% ~33s
|+++++++++++++++++++++++++++ | 54% ~32s
|++++++++++++++++++++++++++++ | 55% ~31s
|++++++++++++++++++++++++++++ | 56% ~30s
|+++++++++++++++++++++++++++++ | 57% ~29s
|++++++++++++++++++++++++++++++ | 58% ~29s
|++++++++++++++++++++++++++++++ | 60% ~28s
|+++++++++++++++++++++++++++++++ | 61% ~27s
|+++++++++++++++++++++++++++++++ | 62% ~26s
|++++++++++++++++++++++++++++++++ | 63% ~25s
|+++++++++++++++++++++++++++++++++ | 64% ~24s
|+++++++++++++++++++++++++++++++++ | 65% ~24s
|++++++++++++++++++++++++++++++++++ | 67% ~23s
|++++++++++++++++++++++++++++++++++ | 68% ~22s
|+++++++++++++++++++++++++++++++++++ | 69% ~21s
|++++++++++++++++++++++++++++++++++++ | 70% ~20s
|++++++++++++++++++++++++++++++++++++ | 71% ~20s
|+++++++++++++++++++++++++++++++++++++ | 73% ~19s
|+++++++++++++++++++++++++++++++++++++ | 74% ~18s
|++++++++++++++++++++++++++++++++++++++ | 75% ~17s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~16s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~14s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~13s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~10s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 09sCalculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~14s
|++ | 3 % ~14s
|++ | 4 % ~14s
|+++ | 5 % ~14s
|++++ | 7 % ~13s
|++++ | 8 % ~13s
|+++++ | 9 % ~13s
|++++++ | 11% ~13s
|++++++ | 12% ~13s
|+++++++ | 13% ~13s
|++++++++ | 15% ~12s
|++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|++++++++++ | 19% ~12s
|++++++++++ | 20% ~12s
|+++++++++++ | 21% ~11s
|++++++++++++ | 23% ~11s
|++++++++++++ | 24% ~11s
|+++++++++++++ | 25% ~11s
|++++++++++++++ | 27% ~11s
|++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|++++++++++++++++ | 31% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|++++++++++++++++++ | 35% ~10s
|++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~09s
|++++++++++++++++++++ | 39% ~09s
|++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~09s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 53% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=16s Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~01m 38s
|++ | 2 % ~01m 38s
|++ | 3 % ~01m 36s
|+++ | 4 % ~01m 35s
|+++ | 5 % ~01m 33s
|++++ | 6 % ~01m 32s
|++++ | 7 % ~01m 32s
|+++++ | 8 % ~01m 32s
|+++++ | 9 % ~01m 32s
|++++++ | 10% ~01m 31s
|++++++ | 11% ~01m 30s
|+++++++ | 12% ~01m 30s
|+++++++ | 14% ~01m 29s
|++++++++ | 15% ~01m 28s
|++++++++ | 16% ~01m 27s
|+++++++++ | 17% ~01m 26s
|+++++++++ | 18% ~01m 25s
|++++++++++ | 19% ~01m 26s
|++++++++++ | 20% ~01m 25s
|+++++++++++ | 21% ~01m 23s
|+++++++++++ | 22% ~01m 22s
|++++++++++++ | 23% ~01m 21s
|++++++++++++ | 24% ~01m 20s
|+++++++++++++ | 25% ~01m 18s
|++++++++++++++ | 26% ~01m 17s
|++++++++++++++ | 27% ~01m 16s
|+++++++++++++++ | 28% ~01m 15s
|+++++++++++++++ | 29% ~01m 14s
|++++++++++++++++ | 30% ~01m 12s
|++++++++++++++++ | 31% ~01m 11s
|+++++++++++++++++ | 32% ~01m 10s
|+++++++++++++++++ | 33% ~01m 09s
|++++++++++++++++++ | 34% ~01m 08s
|++++++++++++++++++ | 35% ~01m 07s
|+++++++++++++++++++ | 36% ~01m 06s
|+++++++++++++++++++ | 38% ~01m 06s
|++++++++++++++++++++ | 39% ~01m 04s
|++++++++++++++++++++ | 40% ~01m 03s
|+++++++++++++++++++++ | 41% ~01m 02s
|+++++++++++++++++++++ | 42% ~01m 01s
|++++++++++++++++++++++ | 43% ~60s
|++++++++++++++++++++++ | 44% ~59s
|+++++++++++++++++++++++ | 45% ~58s
|+++++++++++++++++++++++ | 46% ~57s
|++++++++++++++++++++++++ | 47% ~55s
|++++++++++++++++++++++++ | 48% ~54s
|+++++++++++++++++++++++++ | 49% ~53s
|+++++++++++++++++++++++++ | 50% ~52s
|++++++++++++++++++++++++++ | 51% ~51s
|+++++++++++++++++++++++++++ | 52% ~50s
|+++++++++++++++++++++++++++ | 53% ~49s
|++++++++++++++++++++++++++++ | 54% ~48s
|++++++++++++++++++++++++++++ | 55% ~47s
|+++++++++++++++++++++++++++++ | 56% ~46s
|+++++++++++++++++++++++++++++ | 57% ~45s
|++++++++++++++++++++++++++++++ | 58% ~44s
|++++++++++++++++++++++++++++++ | 59% ~42s
|+++++++++++++++++++++++++++++++ | 60% ~41s
|+++++++++++++++++++++++++++++++ | 61% ~40s
|++++++++++++++++++++++++++++++++ | 62% ~39s
|++++++++++++++++++++++++++++++++ | 64% ~38s
|+++++++++++++++++++++++++++++++++ | 65% ~37s
|+++++++++++++++++++++++++++++++++ | 66% ~36s
|++++++++++++++++++++++++++++++++++ | 67% ~35s
|++++++++++++++++++++++++++++++++++ | 68% ~34s
|+++++++++++++++++++++++++++++++++++ | 69% ~32s
|+++++++++++++++++++++++++++++++++++ | 70% ~31s
|++++++++++++++++++++++++++++++++++++ | 71% ~30s
|++++++++++++++++++++++++++++++++++++ | 72% ~29s
|+++++++++++++++++++++++++++++++++++++ | 73% ~28s
|+++++++++++++++++++++++++++++++++++++ | 74% ~27s
|++++++++++++++++++++++++++++++++++++++ | 75% ~26s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~25s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~24s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~23s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~21s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~20s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~18s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~17s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~16s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~15s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~14s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 43sCalculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~02m 19s
|++ | 2 % ~02m 17s
|++ | 3 % ~02m 13s
|+++ | 4 % ~02m 11s
|+++ | 5 % ~02m 11s
|++++ | 6 % ~02m 11s
|++++ | 7 % ~02m 10s
|+++++ | 9 % ~02m 09s
|+++++ | 10% ~02m 08s
|++++++ | 11% ~02m 07s
|++++++ | 12% ~02m 05s
|+++++++ | 13% ~02m 04s
|+++++++ | 14% ~02m 05s
|++++++++ | 15% ~02m 03s
|++++++++ | 16% ~02m 01s
|+++++++++ | 17% ~01m 59s
|++++++++++ | 18% ~01m 57s
|++++++++++ | 19% ~01m 55s
|+++++++++++ | 20% ~01m 53s
|+++++++++++ | 21% ~01m 52s
|++++++++++++ | 22% ~01m 51s
|++++++++++++ | 23% ~01m 49s
|+++++++++++++ | 24% ~01m 48s
|+++++++++++++ | 26% ~01m 46s
|++++++++++++++ | 27% ~01m 45s
|++++++++++++++ | 28% ~01m 44s
|+++++++++++++++ | 29% ~01m 42s
|+++++++++++++++ | 30% ~01m 40s
|++++++++++++++++ | 31% ~01m 39s
|++++++++++++++++ | 32% ~01m 37s
|+++++++++++++++++ | 33% ~01m 35s
|++++++++++++++++++ | 34% ~01m 33s
|++++++++++++++++++ | 35% ~01m 32s
|+++++++++++++++++++ | 36% ~01m 30s
|+++++++++++++++++++ | 37% ~01m 29s
|++++++++++++++++++++ | 38% ~01m 27s
|++++++++++++++++++++ | 39% ~01m 26s
|+++++++++++++++++++++ | 40% ~01m 25s
|+++++++++++++++++++++ | 41% ~01m 24s
|++++++++++++++++++++++ | 43% ~01m 22s
|++++++++++++++++++++++ | 44% ~01m 21s
|+++++++++++++++++++++++ | 45% ~01m 19s
|+++++++++++++++++++++++ | 46% ~01m 17s
|++++++++++++++++++++++++ | 47% ~01m 16s
|++++++++++++++++++++++++ | 48% ~01m 14s
|+++++++++++++++++++++++++ | 49% ~01m 13s
|+++++++++++++++++++++++++ | 50% ~01m 11s
|++++++++++++++++++++++++++ | 51% ~01m 10s
|+++++++++++++++++++++++++++ | 52% ~01m 08s
|+++++++++++++++++++++++++++ | 53% ~01m 07s
|++++++++++++++++++++++++++++ | 54% ~01m 05s
|++++++++++++++++++++++++++++ | 55% ~01m 04s
|+++++++++++++++++++++++++++++ | 56% ~01m 02s
|+++++++++++++++++++++++++++++ | 57% ~01m 01s
|++++++++++++++++++++++++++++++ | 59% ~59s
|++++++++++++++++++++++++++++++ | 60% ~58s
|+++++++++++++++++++++++++++++++ | 61% ~56s
|+++++++++++++++++++++++++++++++ | 62% ~54s
|++++++++++++++++++++++++++++++++ | 63% ~53s
|++++++++++++++++++++++++++++++++ | 64% ~51s
|+++++++++++++++++++++++++++++++++ | 65% ~50s
|+++++++++++++++++++++++++++++++++ | 66% ~48s
|++++++++++++++++++++++++++++++++++ | 67% ~47s
|+++++++++++++++++++++++++++++++++++ | 68% ~45s
|+++++++++++++++++++++++++++++++++++ | 69% ~44s
|++++++++++++++++++++++++++++++++++++ | 70% ~42s
|++++++++++++++++++++++++++++++++++++ | 71% ~41s
|+++++++++++++++++++++++++++++++++++++ | 72% ~39s
|+++++++++++++++++++++++++++++++++++++ | 73% ~38s
|++++++++++++++++++++++++++++++++++++++ | 74% ~36s
|++++++++++++++++++++++++++++++++++++++ | 76% ~35s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~33s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~32s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~30s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~29s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~27s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~26s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~24s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~23s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~21s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~20s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~15s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~12s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 22sCalculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~45s
|++ | 2 % ~47s
|++ | 3 % ~45s
|+++ | 4 % ~45s
|+++ | 5 % ~44s
|++++ | 7 % ~44s
|++++ | 8 % ~43s
|+++++ | 9 % ~42s
|+++++ | 10% ~42s
|++++++ | 11% ~41s
|++++++ | 12% ~40s
|+++++++ | 13% ~40s
|++++++++ | 14% ~39s
|++++++++ | 15% ~39s
|+++++++++ | 16% ~39s
|+++++++++ | 17% ~38s
|++++++++++ | 18% ~38s
|++++++++++ | 20% ~38s
|+++++++++++ | 21% ~37s
|+++++++++++ | 22% ~37s
|++++++++++++ | 23% ~36s
|++++++++++++ | 24% ~36s
|+++++++++++++ | 25% ~36s
|++++++++++++++ | 26% ~35s
|++++++++++++++ | 27% ~35s
|+++++++++++++++ | 28% ~34s
|+++++++++++++++ | 29% ~35s
|++++++++++++++++ | 30% ~34s
|++++++++++++++++ | 32% ~34s
|+++++++++++++++++ | 33% ~33s
|+++++++++++++++++ | 34% ~32s
|++++++++++++++++++ | 35% ~32s
|++++++++++++++++++ | 36% ~31s
|+++++++++++++++++++ | 37% ~31s
|++++++++++++++++++++ | 38% ~30s
|++++++++++++++++++++ | 39% ~29s
|+++++++++++++++++++++ | 40% ~29s
|+++++++++++++++++++++ | 41% ~28s
|++++++++++++++++++++++ | 42% ~28s
|++++++++++++++++++++++ | 43% ~27s
|+++++++++++++++++++++++ | 45% ~27s
|+++++++++++++++++++++++ | 46% ~26s
|++++++++++++++++++++++++ | 47% ~26s
|++++++++++++++++++++++++ | 48% ~25s
|+++++++++++++++++++++++++ | 49% ~25s
|+++++++++++++++++++++++++ | 50% ~24s
|++++++++++++++++++++++++++ | 51% ~23s
|+++++++++++++++++++++++++++ | 52% ~23s
|+++++++++++++++++++++++++++ | 53% ~22s
|++++++++++++++++++++++++++++ | 54% ~22s
|++++++++++++++++++++++++++++ | 55% ~21s
|+++++++++++++++++++++++++++++ | 57% ~21s
|+++++++++++++++++++++++++++++ | 58% ~20s
|++++++++++++++++++++++++++++++ | 59% ~20s
|++++++++++++++++++++++++++++++ | 60% ~19s
|+++++++++++++++++++++++++++++++ | 61% ~19s
|+++++++++++++++++++++++++++++++ | 62% ~19s
|++++++++++++++++++++++++++++++++ | 63% ~18s
|+++++++++++++++++++++++++++++++++ | 64% ~18s
|+++++++++++++++++++++++++++++++++ | 65% ~17s
|++++++++++++++++++++++++++++++++++ | 66% ~16s
|++++++++++++++++++++++++++++++++++ | 67% ~16s
|+++++++++++++++++++++++++++++++++++ | 68% ~15s
|+++++++++++++++++++++++++++++++++++ | 70% ~15s
|++++++++++++++++++++++++++++++++++++ | 71% ~14s
|++++++++++++++++++++++++++++++++++++ | 72% ~14s
|+++++++++++++++++++++++++++++++++++++ | 73% ~13s
|+++++++++++++++++++++++++++++++++++++ | 74% ~13s
|++++++++++++++++++++++++++++++++++++++ | 75% ~12s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~12s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~10s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~09s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~08s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~07s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=48s Calculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~02m 51s
|++ | 2 % ~02m 51s
|++ | 3 % ~02m 48s
|+++ | 4 % ~02m 44s
|+++ | 5 % ~02m 42s
|++++ | 6 % ~02m 43s
|++++ | 7 % ~02m 42s
|+++++ | 8 % ~02m 41s
|+++++ | 9 % ~02m 43s
|++++++ | 11% ~02m 40s
|++++++ | 12% ~02m 38s
|+++++++ | 13% ~02m 36s
|+++++++ | 14% ~02m 33s
|++++++++ | 15% ~02m 31s
|++++++++ | 16% ~02m 29s
|+++++++++ | 17% ~02m 27s
|+++++++++ | 18% ~02m 26s
|++++++++++ | 19% ~02m 26s
|++++++++++ | 20% ~02m 25s
|+++++++++++ | 21% ~02m 22s
|++++++++++++ | 22% ~02m 20s
|++++++++++++ | 23% ~02m 18s
|+++++++++++++ | 24% ~02m 15s
|+++++++++++++ | 25% ~02m 13s
|++++++++++++++ | 26% ~02m 11s
|++++++++++++++ | 27% ~02m 09s
|+++++++++++++++ | 28% ~02m 07s
|+++++++++++++++ | 29% ~02m 06s
|++++++++++++++++ | 31% ~02m 04s
|++++++++++++++++ | 32% ~02m 02s
|+++++++++++++++++ | 33% ~02m 00s
|+++++++++++++++++ | 34% ~01m 59s
|++++++++++++++++++ | 35% ~01m 57s
|++++++++++++++++++ | 36% ~01m 56s
|+++++++++++++++++++ | 37% ~01m 54s
|+++++++++++++++++++ | 38% ~01m 52s
|++++++++++++++++++++ | 39% ~01m 50s
|++++++++++++++++++++ | 40% ~01m 48s
|+++++++++++++++++++++ | 41% ~01m 46s
|++++++++++++++++++++++ | 42% ~01m 44s
|++++++++++++++++++++++ | 43% ~01m 42s
|+++++++++++++++++++++++ | 44% ~01m 40s
|+++++++++++++++++++++++ | 45% ~01m 39s
|++++++++++++++++++++++++ | 46% ~01m 37s
|++++++++++++++++++++++++ | 47% ~01m 35s
|+++++++++++++++++++++++++ | 48% ~01m 33s
|+++++++++++++++++++++++++ | 49% ~01m 31s
|++++++++++++++++++++++++++ | 51% ~01m 29s
|++++++++++++++++++++++++++ | 52% ~01m 27s
|+++++++++++++++++++++++++++ | 53% ~01m 26s
|+++++++++++++++++++++++++++ | 54% ~01m 24s
|++++++++++++++++++++++++++++ | 55% ~01m 22s
|++++++++++++++++++++++++++++ | 56% ~01m 20s
|+++++++++++++++++++++++++++++ | 57% ~01m 18s
|+++++++++++++++++++++++++++++ | 58% ~01m 16s
|++++++++++++++++++++++++++++++ | 59% ~01m 14s
|++++++++++++++++++++++++++++++ | 60% ~01m 12s
|+++++++++++++++++++++++++++++++ | 61% ~01m 11s
|++++++++++++++++++++++++++++++++ | 62% ~01m 09s
|++++++++++++++++++++++++++++++++ | 63% ~01m 07s
|+++++++++++++++++++++++++++++++++ | 64% ~01m 05s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 03s
|++++++++++++++++++++++++++++++++++ | 66% ~01m 01s
|++++++++++++++++++++++++++++++++++ | 67% ~59s
|+++++++++++++++++++++++++++++++++++ | 68% ~57s
|+++++++++++++++++++++++++++++++++++ | 69% ~56s
|++++++++++++++++++++++++++++++++++++ | 71% ~54s
|++++++++++++++++++++++++++++++++++++ | 72% ~52s
|+++++++++++++++++++++++++++++++++++++ | 73% ~50s
|+++++++++++++++++++++++++++++++++++++ | 74% ~48s
|++++++++++++++++++++++++++++++++++++++ | 75% ~46s
|++++++++++++++++++++++++++++++++++++++ | 76% ~44s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~42s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~40s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~39s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~37s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~35s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~33s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~31s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~29s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~27s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~25s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~21s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~19s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~17s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 04ssave(all_data.markers, cis_markers, cocl2_markers, dabtram_markers, file = 'all_data_markers.RData')#find lineages that are maintained at both dabtram timepoints
fivecell_cDNA$DabTramMaintained <- Reduce(intersect, list(fivecell_cDNA$DabTram, fivecell_cDNA$DabTramtoDabTram))
filtered_meta <- rep(0, length(names(all_data$Lineage)))
#specify which cells are in lineages that pass filtering for that condition
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% combined_lins_list$DabTram)] <- 'Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% combined_lins_list$DabTramtoDabTram)] <- 'Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% combined_lins_list$DabTramtoCoCl2)] <- 'Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% combined_lins_list$DabTramtoCis)] <- 'Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% combined_lins_list$CoCl2)] <- 'Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% combined_lins_list$CoCl2toDabTram)] <- 'Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% combined_lins_list$CoCl2toCoCl2)] <- 'Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% combined_lins_list$CoCl2toCis)] <- 'Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% combined_lins_list$Cis)] <- 'Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% combined_lins_list$CistoDabTram)] <- 'Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% combined_lins_list$CistoCoCl2)] <- 'Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% combined_lins_list$CistoCis)] <- 'Resistant to CistoCis'
#specify which cells are in lineages of more than 5 cells
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTram)] <- 'Large Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramtoDabTram)] <- 'Large Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCoCl2)] <- 'Large Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCis)] <- 'Large Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2)] <- 'Large Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% fivecell_cDNA$CoCl2toDabTram)] <- 'Large Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCoCl2)] <- 'Large Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCis)] <- 'Large Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% fivecell_cDNA$Cis)] <- 'Large Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% fivecell_cDNA$CistoDabTram)] <- 'Large Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% fivecell_cDNA$CistoCoCl2)] <- 'Large Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% fivecell_cDNA$CistoCis)] <- 'Large Resistant to CistoCis'
# filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTram'
# filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTramtoDabTram'
#specify which cells are in lineages that did not pass filtering
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %nin% combined_lins_list$DabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %nin% combined_lins_list$DabTramtoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %nin% combined_lins_list$DabTramtoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %nin% combined_lins_list$DabTramtoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %nin% combined_lins_list$CoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %nin% combined_lins_list$CoCl2toDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %nin% combined_lins_list$CoCl2toCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %nin% combined_lins_list$CoCl2toCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %nin% combined_lins_list$Cis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %nin% combined_lins_list$CistoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %nin% combined_lins_list$CistoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %nin% combined_lins_list$CistoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out'
#specify which cells had zero or multiple barcodes
filtered_meta[which(all_data$Lineage %in% c("No Barcode", "Still multiple"))] <- 'No Barcode'
print(table(filtered_meta))filtered_meta
Filtered out Large Resistant to Cis Large Resistant to CistoCis Large Resistant to CistoCoCl2 Large Resistant to CistoDabTram
3337 1375 951 2078 1394
Large Resistant to CoCl2 Large Resistant to CoCl2toCis Large Resistant to CoCl2toCoCl2 Large Resistant to CoCl2toDabTram Large Resistant to DabTram
1784 3010 11578 663 478
Large Resistant to DabTramtoCis Large Resistant to DabTramtoCoCl2 Large Resistant to DabTramtoDabTram No Barcode Resistant to Cis
4234 2840 3176 35314 331
Resistant to CistoCis Resistant to CistoCoCl2 Resistant to CistoDabTram Resistant to CoCl2 Resistant to CoCl2toCis
67 135 113 278 157
Resistant to CoCl2toCoCl2 Resistant to CoCl2toDabTram Resistant to DabTram Resistant to DabTramtoCis Resistant to DabTramtoCoCl2
55 93 337 225 100
Resistant to DabTramtoDabTram
222 all_data$Resistant_filtered <- filtered_meta
Idents(all_data) <- all_data$Resistant_filteredpdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_plot.pdf', height = 10, width = 20)
DimPlot(all_data, group.by = 'ident', cols = )
DimPlot(all_data, group.by = "Lineage") + theme(legend.position = 'none')dev.off()null device
1 #Looking into the one lineage that switches ngfr -> egfr
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_violin.pdf', height = 10, width = 30)
VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of NGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning InfVlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of EGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning InfVlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of NGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning InfVlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of EGFR.
Warning in max(data[, feature][is.finite(x = data[, feature])]) :
no non-missing arguments to max; returning -Inf
Warning in min(data[, feature]) :
no non-missing arguments to min; returning InfVlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
dev.off()null device
1 #VlnPlot(all_data, features = 'NGFR', idents = all_data$OG_condition == 'dabtram', group.by = "lineage")#from dabtram_both_times, force 2 clusters in umap, plot vs ngfr egfr, find markers of these 2
switch_lin_dabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtram'])
switch_lin_dabtramtodabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtramtodabtram'])
DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram), cols.highlight = c('red'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram, switch_lin_dabtramtodabtram), cols.highlight = c('blue', 'red'))
switch_lin <- names(dabtram$orig.ident[dabtram$Lineage == "Lin171516"])
DimPlot(dabtram)
DimPlot(dabtram, cells.highlight = list(switch_lin))
#need to integrate lineages into just dabtram object and then plot off of this isntead?
#vlnplot(all_data, features = ngfr, idents = just the cells resistant to dabtram, group.by = lineage but i only want those included in combined_lins_list$dabtram ?
#Assign cluster assignments per lineage, find average score per lineage - make plots in order
dabtram_both_times_markers <- FindAllMarkers(dabtram_both_times, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)Calculating cluster 0
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DimPlot(dabtram_both_times, group.by = 'OG_condition')
FeaturePlot(dabtram_both_times, features = c('NGFR', 'EGFR', 'nFeature_RNA'))
Look at whether lineages cluster together in each individual condition - Starting with DabTram
#average cell assignments per lineage in dabtram_maintained
#want to do a stacked barplot here-- copy paste code from condition clustering
#get lineage and cluster data from seurat object, switch cluster identifiers from 0,1 to -1,1 (egfr, ngfr)
clusters_per_lin <- data.frame (Cluster = as.numeric(as.character(dabtram_both_times$seurat_clusters)), Lineage = dabtram_both_times$Lineage, condition = dabtram_both_times$OG_condition)
clusters_per_lin$Cluster[clusters_per_lin$Cluster == 0] <- -1
clusters_per_lin_list <- list()
# Need to get percent values of EGFR for the lineage after the first treatment
for (i in fivecell_cDNA$DabTram){
currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')
Var1 <- c(-1,1)
Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == -1), # EGFR
sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == 1))
clusters_per_lin_list[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
clusters_per_lin_list[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list[[i]]$Var1)), clusters_per_lin_list[[i]]$Freq)
}
# Build a dataframe for plotting based on % EGFR or NGFR per lineage after first treatment
clusters_per_lin_df <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score')
for (i in names(clusters_per_lin_list)){
clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
}
# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df <- clusters_per_lin_df[with(clusters_per_lin_df, order(-Score,Num_cells )),]
clusters_per_lin_df$Lineage <- factor(clusters_per_lin_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))
# make list object for after second treatment
clusters_per_lin_list_sec <- list()
# Need to get percent values of EGFR for the lineage after the second treatment
for (i in fivecell_cDNA$DabTram){
currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')
Var1 <- c(-1,1)
Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == -1), # EGFR
sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == 1))
clusters_per_lin_list_sec[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
clusters_per_lin_list_sec[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list_sec[[i]]$Var1)), clusters_per_lin_list_sec[[i]]$Freq)
}
# Build a dataframe for plotting based on % EGFR or NGFR per lineage after second treatment
clusters_per_lin_df_sec <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df_sec) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score', 'Cluster')
for (i in names(clusters_per_lin_list)){
if (sum(clusters_per_lin_list_sec[[i]]$Freq) < 5){
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 1, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'Died')) # Add Lineage died row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'NGFR')) # Add NGFR row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'EGFR')) # Add EGFR row
}else{ # Actually calculate percentages since the lineage survived/is more than 5 cells
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'Died')) # Add Lineage died row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
}
}
# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df_sec$Lineage <- factor(clusters_per_lin_df_sec$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))
# Plot
p1 <- ggplot(clusters_per_lin_df, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#000000","#FFFFFF")) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p2 <- ggplot(clusters_per_lin_df_sec, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#FF0000","#000000", '#FFFFFF')) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died.pdf')
ggarrange(p1,p2, nrow = 2)
dev.off()null device
1 Significance testing of the dabtram simulation
rm(dabtram, cis, cocl2) # Remove old seurat objects that are no longer needed
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtram
dabtram <- subset(all_data, idents = 'dabtram') # Subset down to the dabtram object
ElbowPlot(dabtram) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
dabtram <- FindNeighbors(dabtram, dims = 1:10)Computing nearest neighbor graph
Computing SNNdabtram <- FindClusters(dabtram, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3203
Number of edges: 108151
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8611
Number of communities: 7
Elapsed time: 0 secondsdabtram <- RunUMAP(dabtram, dims = 1:10)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
12:11:39 UMAP embedding parameters a = 0.9922 b = 1.112
12:11:39 Read 3203 rows and found 10 numeric columns
12:11:39 Using Annoy for neighbor search, n_neighbors = 30
12:11:39 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:11:39 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd26820f76
12:11:39 Searching Annoy index using 1 thread, search_k = 3000
12:11:40 Annoy recall = 100%
12:11:40 Commencing smooth kNN distance calibration using 1 thread
12:11:41 Initializing from normalized Laplacian + noise
12:11:42 Commencing optimization for 500 epochs, with 133908 positive edges
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:11:46 Optimization finishedDimPlot(dabtram, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
dabtram_lin_clust_list <- list()
for (i in fivecell_cDNA$DabTram){
temp_df <- as.data.frame(table(Idents(dabtram)[dabtram$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtram_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
dabtram_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
dabtram_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$DabTram){
num_cells <- length(dabtram$Lineage[dabtram$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(dabtram), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - DabTramtoDabTram
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtram <- mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtram <- (mean_dabtram-mean(means_dabtram_sim))/sd(means_dabtram_sim)
pval_mean_dabtram <- pnorm(z_mean_dabtram, mean(means_dabtram_sim), sd(means_dabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtram <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtram <- (weighted_mean_dabtram-mean(weighted_means_dabtram_sim))/sd(weighted_means_dabtram_sim)
pval_wmean_dabtram <- pnorm(z_wmean_dabtram, mean(weighted_means_dabtram_sim), sd(weighted_means_dabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtram <- c()
for (i in 1:length(means_dabtram_sim)){
sim_maxes <- unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtram <- cbind(ttest_pval_dabtram,t.test(x = unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtram_lin_clust_list, dabtram_lin_clust_rand_list,z_mean_dabtram,pval_mean_dabtram, z_wmean_dabtram, ttest_pval_dabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
rm(dabtram)Significance testing of the dabtramtodabtram simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtodabtram
dabtramtodabtram <- subset(all_data, idents = 'dabtramtodabtram') # Subset down to the dabtramtodabtram object
ElbowPlot(dabtramtodabtram) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
dabtramtodabtram <- FindNeighbors(dabtramtodabtram, dims = 1:10)Computing nearest neighbor graph
Computing SNNdabtramtodabtram <- FindClusters(dabtramtodabtram, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 6906
Number of edges: 222798
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8263
Number of communities: 7
Elapsed time: 0 secondsdabtramtodabtram <- RunUMAP(dabtramtodabtram, dims = 1:10)12:15:17 UMAP embedding parameters a = 0.9922 b = 1.112
12:15:17 Read 6906 rows and found 10 numeric columns
12:15:17 Using Annoy for neighbor search, n_neighbors = 30
12:15:17 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:15:18 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd24c51fba
12:15:18 Searching Annoy index using 1 thread, search_k = 3000
12:15:20 Annoy recall = 100%
12:15:20 Commencing smooth kNN distance calibration using 1 thread
12:15:21 Initializing from normalized Laplacian + noise
12:15:21 Commencing optimization for 500 epochs, with 286280 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:15:30 Optimization finishedDimPlot(dabtramtodabtram, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
dabtramtodabtram_lin_clust_list <- list()
for (i in fivecell_cDNA$DabTramtoDabTram){
temp_df <- as.data.frame(table(Idents(dabtramtodabtram)[dabtramtodabtram$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtodabtram_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
dabtramtodabtram_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
dabtramtodabtram_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$DabTramtoDabTram){
num_cells <- length(dabtramtodabtram$Lineage[dabtramtodabtram$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(dabtramtodabtram), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - DabTramtoCis
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtodabtram <- mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtodabtram <- (mean_dabtramtodabtram-mean(means_dabtramtodabtram_sim))/sd(means_dabtramtodabtram_sim)
pval_mean_dabtramtodabtram <- pnorm(z_mean_dabtramtodabtram, mean(means_dabtramtodabtram_sim), sd(means_dabtramtodabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtodabtram <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtodabtram <- (weighted_mean_dabtramtodabtram-mean(weighted_means_dabtramtodabtram_sim))/sd(weighted_means_dabtramtodabtram_sim)
pval_wmean_dabtramtodabtram <- pnorm(z_wmean_dabtramtodabtram, mean(weighted_means_dabtramtodabtram_sim), sd(weighted_means_dabtramtodabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtodabtram <- c()
for (i in 1:length(means_dabtramtodabtram_sim)){
sim_maxes <- unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtramtodabtram <- cbind(ttest_pval_dabtramtodabtram,t.test(x = unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtramtodabtram_lin_clust_list, dabtramtodabtram_lin_clust_rand_list,z_mean_dabtramtodabtram,pval_mean_dabtramtodabtram, z_wmean_dabtramtodabtram, ttest_pval_dabtramtodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
rm(dabtramtodabtram)Significance testing of the dabtramtocis simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtocis
dabtramtocis <- subset(all_data, idents = 'dabtramtocis') # Subset down to the dabtramtocis object
ElbowPlot(dabtramtocis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
dabtramtocis <- FindNeighbors(dabtramtocis, dims = 1:10)Computing nearest neighbor graph
Computing SNNdabtramtocis <- FindClusters(dabtramtocis, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 7070
Number of edges: 235249
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8291
Number of communities: 7
Elapsed time: 0 secondsdabtramtocis <- RunUMAP(dabtramtocis, dims = 1:10)12:22:56 UMAP embedding parameters a = 0.9922 b = 1.112
12:22:56 Read 7070 rows and found 10 numeric columns
12:22:56 Using Annoy for neighbor search, n_neighbors = 30
12:22:56 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:22:57 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd9a5ff32
12:22:57 Searching Annoy index using 1 thread, search_k = 3000
12:22:59 Annoy recall = 100%
12:23:00 Commencing smooth kNN distance calibration using 1 thread
12:23:01 Initializing from normalized Laplacian + noise
12:23:01 Commencing optimization for 500 epochs, with 294448 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:23:10 Optimization finishedDimPlot(dabtramtocis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
dabtramtocis_lin_clust_list <- list()
for (i in fivecell_cDNA$DabTramtoCis){
temp_df <- as.data.frame(table(Idents(dabtramtocis)[dabtramtocis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtocis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
dabtramtocis_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
dabtramtocis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$DabTramtoCis){
num_cells <- length(dabtramtocis$Lineage[dabtramtocis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(dabtramtocis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtocis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - DabTramtoCis
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtocis <- mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtocis <- (mean_dabtramtocis-mean(means_dabtramtocis_sim))/sd(means_dabtramtocis_sim)
pval_mean_dabtramtocis <- pnorm(z_mean_dabtramtocis, mean(means_dabtramtocis_sim), sd(means_dabtramtocis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtocis <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtocis <- (weighted_mean_dabtramtocis-mean(weighted_means_dabtramtocis_sim))/sd(weighted_means_dabtramtocis_sim)
pval_wmean_dabtramtocis <- pnorm(z_wmean_dabtramtocis, mean(weighted_means_dabtramtocis_sim), sd(weighted_means_dabtramtocis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtocis <- c()
for (i in 1:length(means_dabtramtocis_sim)){
sim_maxes <- unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtramtocis <- cbind(ttest_pval_dabtramtocis,t.test(x = unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtramtocis_lin_clust_list, dabtramtocis_lin_clust_rand_list,z_mean_dabtramtocis,pval_mean_dabtramtocis, z_wmean_dabtramtocis, ttest_pval_dabtramtocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
rm(dabtramtocis)Significance testing of the dabtramtococl2 simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtococl2
dabtramtococl2 <- subset(all_data, idents = 'dabtramtococl2') # Subset down to the dabtramtococl2 object
ElbowPlot(dabtramtococl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
dabtramtococl2 <- FindNeighbors(dabtramtococl2, dims = 1:10)Computing nearest neighbor graph
Computing SNNdabtramtococl2 <- FindClusters(dabtramtococl2, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 10184
Number of edges: 308721
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8258
Number of communities: 8
Elapsed time: 1 secondsdabtramtococl2 <- RunUMAP(dabtramtococl2, dims = 1:10)12:33:35 UMAP embedding parameters a = 0.9922 b = 1.112
12:33:35 Read 10184 rows and found 10 numeric columns
12:33:35 Using Annoy for neighbor search, n_neighbors = 30
12:33:35 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:33:36 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd71153090
12:33:36 Searching Annoy index using 1 thread, search_k = 3000
12:33:39 Annoy recall = 100%
12:33:39 Commencing smooth kNN distance calibration using 1 thread
12:33:41 Initializing from normalized Laplacian + noise
12:33:41 Commencing optimization for 200 epochs, with 415696 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:33:47 Optimization finishedDimPlot(dabtramtococl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
dabtramtococl2_lin_clust_list <- list()
for (i in fivecell_cDNA$DabTramtoCoCl2){
temp_df <- as.data.frame(table(Idents(dabtramtococl2)[dabtramtococl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtococl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
dabtramtococl2_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
dabtramtococl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$DabTramtoCoCl2){
num_cells <- length(dabtramtococl2$Lineage[dabtramtococl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(dabtramtococl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
dabtramtococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - Starting with DabTram
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtococl2 <- mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtococl2 <- (mean_dabtramtococl2-mean(means_dabtramtococl2_sim))/sd(means_dabtramtococl2_sim)
pval_mean_dabtramtococl2 <- pnorm(z_mean_dabtramtococl2, mean(means_dabtramtococl2_sim), sd(means_dabtramtococl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtococl2 <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtococl2 <- (weighted_mean_dabtramtococl2-mean(weighted_means_dabtramtococl2_sim))/sd(weighted_means_dabtramtococl2_sim)
pval_wmean_dabtramtococl2 <- pnorm(z_wmean_dabtramtococl2, mean(weighted_means_dabtramtococl2_sim), sd(weighted_means_dabtramtococl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtococl2 <- c()
for (i in 1:length(means_dabtramtococl2_sim)){
sim_maxes <- unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_dabtramtococl2 <- cbind(ttest_pval_dabtramtococl2,t.test(x = unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(dabtramtococl2_lin_clust_list, dabtramtococl2_lin_clust_rand_list,z_mean_dabtramtococl2,pval_mean_dabtramtococl2, z_wmean_dabtramtococl2, ttest_pval_dabtramtococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
rm(dabtramtococl2)Significance testing of the cocl2 simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2
cocl2 <- subset(all_data, idents = 'cocl2') # Subset down to the cocl2 object
ElbowPlot(cocl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2 <- FindNeighbors(cocl2, dims = 1:10)Computing nearest neighbor graph
Computing SNNcocl2 <- FindClusters(cocl2, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 4823
Number of edges: 160093
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8374
Number of communities: 9
Elapsed time: 0 secondscocl2 <- RunUMAP(cocl2, dims = 1:10)12:38:44 UMAP embedding parameters a = 0.9922 b = 1.112
12:38:44 Read 4823 rows and found 10 numeric columns
12:38:44 Using Annoy for neighbor search, n_neighbors = 30
12:38:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:38:44 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd262777f0
12:38:44 Searching Annoy index using 1 thread, search_k = 3000
12:38:45 Annoy recall = 100%
12:38:46 Commencing smooth kNN distance calibration using 1 thread
12:38:48 Initializing from normalized Laplacian + noise
12:38:48 Commencing optimization for 500 epochs, with 195924 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:38:55 Optimization finishedDimPlot(cocl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2){
temp_df <- as.data.frame(table(Idents(cocl2)[cocl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
cocl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2){
num_cells <- length(cocl2$Lineage[cocl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCoCl2
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2 <- mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2 <- (mean_cocl2-mean(means_cocl2_sim))/sd(means_cocl2_sim)
pval_mean_cocl2 <- pnorm(z_mean_cocl2, mean(means_cocl2_sim), sd(means_cocl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2 <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2 <- (weighted_mean_cocl2-mean(weighted_means_cocl2_sim))/sd(weighted_means_cocl2_sim)
pval_wmean_cocl2 <- pnorm(z_wmean_cocl2, mean(weighted_means_cocl2_sim), sd(weighted_means_cocl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2 <- c()
for (i in 1:length(means_cocl2_sim)){
sim_maxes <- unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2 <- cbind(ttest_pval_cocl2,t.test(x = unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2_lin_clust_list, cocl2_lin_clust_rand_list,z_mean_cocl2,pval_mean_cocl2, z_wmean_cocl2, ttest_pval_cocl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
rm(cocl2)Significance testing of the cocl2tococl2 simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tococl2
cocl2tococl2 <- subset(all_data, idents = 'cocl2tococl2') # Subset down to the cocl2tococl2 object
ElbowPlot(cocl2tococl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2tococl2 <- FindNeighbors(cocl2tococl2, dims = 1:10)Computing nearest neighbor graph
Computing SNNcocl2tococl2 <- FindClusters(cocl2tococl2, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 13951
Number of edges: 395318
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.7954
Number of communities: 6
Elapsed time: 3 secondscocl2tococl2 <- RunUMAP(cocl2tococl2, dims = 1:10)12:43:34 UMAP embedding parameters a = 0.9922 b = 1.112
12:43:34 Read 13951 rows and found 10 numeric columns
12:43:34 Using Annoy for neighbor search, n_neighbors = 30
12:43:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:43:35 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd69334121
12:43:35 Searching Annoy index using 1 thread, search_k = 3000
12:43:40 Annoy recall = 100%
12:43:40 Commencing smooth kNN distance calibration using 1 thread
12:43:42 Initializing from normalized Laplacian + noise
12:43:42 Commencing optimization for 200 epochs, with 586158 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:43:50 Optimization finishedDimPlot(cocl2tococl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2tococl2_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2toCoCl2){
temp_df <- as.data.frame(table(Idents(cocl2tococl2)[cocl2tococl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tococl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2tococl2_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
cocl2tococl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2toCoCl2){
num_cells <- length(cocl2tococl2$Lineage[cocl2tococl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2tococl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCis
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tococl2 <- mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tococl2 <- (mean_cocl2tococl2-mean(means_cocl2tococl2_sim))/sd(means_cocl2tococl2_sim)
pval_mean_cocl2tococl2 <- pnorm(z_mean_cocl2tococl2, mean(means_cocl2tococl2_sim), sd(means_cocl2tococl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tococl2 <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tococl2 <- (weighted_mean_cocl2tococl2-mean(weighted_means_cocl2tococl2_sim))/sd(weighted_means_cocl2tococl2_sim)
pval_wmean_cocl2tococl2 <- pnorm(z_wmean_cocl2tococl2, mean(weighted_means_cocl2tococl2_sim), sd(weighted_means_cocl2tococl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tococl2 <- c()
for (i in 1:length(means_cocl2tococl2_sim)){
sim_maxes <- unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2tococl2 <- cbind(ttest_pval_cocl2tococl2,t.test(x = unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2tococl2_lin_clust_list, cocl2tococl2_lin_clust_rand_list,z_mean_cocl2tococl2,pval_mean_cocl2tococl2, z_wmean_cocl2tococl2, ttest_pval_cocl2tococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
rm(cocl2tococl2)Significance testing of the cocl2tocis simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tocis
cocl2tocis <- subset(all_data, idents = 'cocl2tocis') # Subset down to the cocl2tocis object
ElbowPlot(cocl2tocis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2tocis <- FindNeighbors(cocl2tocis, dims = 1:10)Computing nearest neighbor graph
Computing SNNcocl2tocis <- FindClusters(cocl2tocis, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 6459
Number of edges: 218210
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8279
Number of communities: 6
Elapsed time: 0 secondscocl2tocis <- RunUMAP(cocl2tocis, dims = 1:10)12:45:25 UMAP embedding parameters a = 0.9922 b = 1.112
12:45:25 Read 6459 rows and found 10 numeric columns
12:45:25 Using Annoy for neighbor search, n_neighbors = 30
12:45:25 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:45:25 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd23f9137c
12:45:25 Searching Annoy index using 1 thread, search_k = 3000
12:45:27 Annoy recall = 100%
12:45:28 Commencing smooth kNN distance calibration using 1 thread
12:45:30 Initializing from normalized Laplacian + noise
12:45:30 Commencing optimization for 500 epochs, with 269134 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:45:39 Optimization finishedDimPlot(cocl2tocis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2tocis_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2toCis){
temp_df <- as.data.frame(table(Idents(cocl2tocis)[cocl2tocis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tocis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2tocis_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
cocl2tocis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2toCis){
num_cells <- length(cocl2tocis$Lineage[cocl2tocis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2tocis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2tocis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - Starting with CoCl2toDabTram
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tocis <- mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tocis <- (mean_cocl2tocis-mean(means_cocl2tocis_sim))/sd(means_cocl2tocis_sim)
pval_mean_cocl2tocis <- pnorm(z_mean_cocl2tocis, mean(means_cocl2tocis_sim), sd(means_cocl2tocis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tocis <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tocis <- (weighted_mean_cocl2tocis-mean(weighted_means_cocl2tocis_sim))/sd(weighted_means_cocl2tocis_sim)
pval_wmean_cocl2tocis <- pnorm(z_wmean_cocl2tocis, mean(weighted_means_cocl2tocis_sim), sd(weighted_means_cocl2tocis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tocis <- c()
for (i in 1:length(means_cocl2tocis_sim)){
sim_maxes <- unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2tocis <- cbind(ttest_pval_cocl2tocis,t.test(x = unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2tocis_lin_clust_list, cocl2tocis_lin_clust_rand_list,z_mean_cocl2tocis,pval_mean_cocl2tocis, z_wmean_cocl2tocis, ttest_pval_cocl2tocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
rm(cocl2tocis)Significance testing of the cocl2todabtram simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2todabtram
cocl2todabtram <- subset(all_data, idents = 'cocl2todabtram') # Subset down to the cocl2todabtram object
ElbowPlot(cocl2todabtram) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cocl2todabtram <- FindNeighbors(cocl2todabtram, dims = 1:10)Computing nearest neighbor graph
Computing SNNcocl2todabtram <- FindClusters(cocl2todabtram, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1773
Number of edges: 56087
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8495
Number of communities: 7
Elapsed time: 0 secondscocl2todabtram <- RunUMAP(cocl2todabtram, dims = 1:10)12:50:32 UMAP embedding parameters a = 0.9922 b = 1.112
12:50:32 Read 1773 rows and found 10 numeric columns
12:50:32 Using Annoy for neighbor search, n_neighbors = 30
12:50:32 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:50:33 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd356e4457
12:50:33 Searching Annoy index using 1 thread, search_k = 3000
12:50:33 Annoy recall = 100%
12:50:34 Commencing smooth kNN distance calibration using 1 thread
12:50:36 Initializing from normalized Laplacian + noise
12:50:36 Commencing optimization for 500 epochs, with 72086 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:50:39 Optimization finishedDimPlot(cocl2todabtram, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cocl2todabtram_lin_clust_list <- list()
for (i in fivecell_cDNA$CoCl2toDabTram){
temp_df <- as.data.frame(table(Idents(cocl2todabtram)[cocl2todabtram$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2todabtram_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cocl2todabtram_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
cocl2todabtram_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CoCl2toDabTram){
num_cells <- length(cocl2todabtram$Lineage[cocl2todabtram$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cocl2todabtram), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cocl2todabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - Starting with Cis
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2todabtram <- mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2todabtram <- (mean_cocl2todabtram-mean(means_cocl2todabtram_sim))/sd(means_cocl2todabtram_sim)
pval_mean_cocl2todabtram <- pnorm(z_mean_cocl2todabtram, mean(means_cocl2todabtram_sim), sd(means_cocl2todabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2todabtram <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2todabtram <- (weighted_mean_cocl2todabtram-mean(weighted_means_cocl2todabtram_sim))/sd(weighted_means_cocl2todabtram_sim)
pval_wmean_cocl2todabtram <- pnorm(z_wmean_cocl2todabtram, mean(weighted_means_cocl2todabtram_sim), sd(weighted_means_cocl2todabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2todabtram <- c()
for (i in 1:length(means_cocl2todabtram_sim)){
sim_maxes <- unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cocl2todabtram <- cbind(ttest_pval_cocl2todabtram,t.test(x = unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cocl2todabtram_lin_clust_list, cocl2todabtram_lin_clust_rand_list,z_mean_cocl2todabtram,pval_mean_cocl2todabtram, z_wmean_cocl2todabtram, ttest_pval_cocl2todabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
rm(cocl2todabtram)Significance testing of the cis simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cis
cis <- subset(all_data, idents = 'cis') # Subset down to the cis object
ElbowPlot(cis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cis <- FindNeighbors(cis, dims = 1:10)Computing nearest neighbor graph
Computing SNNcis <- FindClusters(cis, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 8303
Number of edges: 244918
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8046
Number of communities: 8
Elapsed time: 1 secondscis <- RunUMAP(cis, dims = 1:10)12:52:37 UMAP embedding parameters a = 0.9922 b = 1.112
12:52:37 Read 8303 rows and found 10 numeric columns
12:52:37 Using Annoy for neighbor search, n_neighbors = 30
12:52:37 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:52:38 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd5a48c728
12:52:38 Searching Annoy index using 1 thread, search_k = 3000
12:52:41 Annoy recall = 100%
12:52:42 Commencing smooth kNN distance calibration using 1 thread
12:52:43 Initializing from normalized Laplacian + noise
12:52:44 Commencing optimization for 500 epochs, with 350988 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:52:55 Optimization finishedDimPlot(cis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cis_lin_clust_list <- list()
for (i in fivecell_cDNA$Cis){
temp_df <- as.data.frame(table(Idents(cis)[cis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cis_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
cis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$Cis){
num_cells <- length(cis$Lineage[cis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - Starting with cistocis
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cis <- mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cis <- (mean_cis-mean(means_cis_sim))/sd(means_cis_sim)
pval_mean_cis <- pnorm(z_mean_cis, mean(means_cis_sim), sd(means_cis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cis <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cis <- (weighted_mean_cis-mean(weighted_means_cis_sim))/sd(weighted_means_cis_sim)
pval_wmean_cis <- pnorm(z_wmean_cis, mean(weighted_means_cis_sim), sd(weighted_means_cis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cis <- c()
for (i in 1:length(means_cis_sim)){
sim_maxes <- unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cis <- cbind(ttest_pval_cis,t.test(x = unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cis_lin_clust_list, cis_lin_clust_rand_list,z_mean_cis,pval_mean_cis, z_wmean_cis, ttest_pval_cis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
rm(cis)Significance testing of the cistocis simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistocis
cistocis <- subset(all_data, idents = 'cistocis') # Subset down to the cistocis object
ElbowPlot(cistocis) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cistocis <- FindNeighbors(cistocis, dims = 1:10)Computing nearest neighbor graph
Computing SNNcistocis <- FindClusters(cistocis, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 3323
Number of edges: 114057
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8412
Number of communities: 9
Elapsed time: 0 secondscistocis <- RunUMAP(cistocis, dims = 1:10)12:58:20 UMAP embedding parameters a = 0.9922 b = 1.112
12:58:20 Read 3323 rows and found 10 numeric columns
12:58:20 Using Annoy for neighbor search, n_neighbors = 30
12:58:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:58:21 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd600b4166
12:58:21 Searching Annoy index using 1 thread, search_k = 3000
12:58:21 Annoy recall = 100%
12:58:22 Commencing smooth kNN distance calibration using 1 thread
12:58:25 Initializing from normalized Laplacian + noise
12:58:25 Commencing optimization for 500 epochs, with 136790 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:58:30 Optimization finishedDimPlot(cistocis, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cistocis_lin_clust_list <- list()
for (i in fivecell_cDNA$CistoCis){
temp_df <- as.data.frame(table(Idents(cistocis)[cistocis$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistocis_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cistocis_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
cistocis_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CistoCis){
num_cells <- length(cistocis$Lineage[cistocis$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cistocis), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistocis_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - Starting with cistodabtram
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistocis <- mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistocis <- (mean_cistocis-mean(means_cistocis_sim))/sd(means_cistocis_sim)
pval_mean_cistocis <- pnorm(z_mean_cistocis, mean(means_cistocis_sim), sd(means_cistocis_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistocis <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistocis <- (weighted_mean_cistocis-mean(weighted_means_cistocis_sim))/sd(weighted_means_cistocis_sim)
pval_wmean_cistocis <- pnorm(z_wmean_cistocis, mean(weighted_means_cistocis_sim), sd(weighted_means_cistocis_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistocis <- c()
for (i in 1:length(means_cistocis_sim)){
sim_maxes <- unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cistocis <- cbind(ttest_pval_cistocis,t.test(x = unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cistocis_lin_clust_list, cistocis_lin_clust_rand_list,z_mean_cistocis,pval_mean_cistocis, z_wmean_cistocis, ttest_pval_cistocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')
rm(cistocis)Significance testing of the cistodabtram simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistodabtram
cistodabtram <- subset(all_data, idents = 'cistodabtram') # Subset down to the cistodabtram object
ElbowPlot(cistodabtram) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cistodabtram <- FindNeighbors(cistodabtram, dims = 1:10)Computing nearest neighbor graph
Computing SNNcistodabtram <- FindClusters(cistodabtram, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 2615
Number of edges: 86990
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8655
Number of communities: 8
Elapsed time: 0 secondscistodabtram <- RunUMAP(cistodabtram, dims = 1:10)12:59:48 UMAP embedding parameters a = 0.9922 b = 1.112
12:59:48 Read 2615 rows and found 10 numeric columns
12:59:48 Using Annoy for neighbor search, n_neighbors = 30
12:59:48 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:59:48 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bd2f2c0cd
12:59:48 Searching Annoy index using 1 thread, search_k = 3000
12:59:49 Annoy recall = 100%
12:59:50 Commencing smooth kNN distance calibration using 1 thread
12:59:52 Initializing from normalized Laplacian + noise
12:59:52 Commencing optimization for 500 epochs, with 106198 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:59:57 Optimization finishedDimPlot(cistodabtram, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cistodabtram_lin_clust_list <- list()
for (i in fivecell_cDNA$CistoDabTram){
temp_df <- as.data.frame(table(Idents(cistodabtram)[cistodabtram$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistodabtram_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cistodabtram_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
cistodabtram_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CistoDabTram){
num_cells <- length(cistodabtram$Lineage[cistodabtram$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cistodabtram), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Look at whether lineages cluster together in each individual condition - Starting with cistococl2
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistodabtram <- mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistodabtram <- (mean_cistodabtram-mean(means_cistodabtram_sim))/sd(means_cistodabtram_sim)
pval_mean_cistodabtram <- pnorm(z_mean_cistodabtram, mean(means_cistodabtram_sim), sd(means_cistodabtram_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistodabtram <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistodabtram <- (weighted_mean_cistodabtram-mean(weighted_means_cistodabtram_sim))/sd(weighted_means_cistodabtram_sim)
pval_wmean_cistodabtram <- pnorm(z_wmean_cistodabtram, mean(weighted_means_cistodabtram_sim), sd(weighted_means_cistodabtram_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistodabtram <- c()
for (i in 1:length(means_cistodabtram_sim)){
sim_maxes <- unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cistodabtram <- cbind(ttest_pval_cistodabtram,t.test(x = unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cistodabtram_lin_clust_list, cistodabtram_lin_clust_rand_list,z_mean_cistodabtram,pval_mean_cistodabtram, z_wmean_cistodabtram, ttest_pval_cistodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
rm(cistodabtram)Significance testing of the cistococl2 simulation
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistococl2
cistococl2 <- subset(all_data, idents = 'cistococl2') # Subset down to the cistococl2 object
ElbowPlot(cistococl2) # The standard deviation seems to really level off at 10
# Recluster with the appropriate number of dimensions
cistococl2 <- FindNeighbors(cistococl2, dims = 1:10)Computing nearest neighbor graph
Computing SNNcistococl2 <- FindClusters(cistococl2, resolution = 0.5)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 5715
Number of edges: 185463
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.8313
Number of communities: 7
Elapsed time: 0 secondscistococl2 <- RunUMAP(cistococl2, dims = 1:10)13:01:13 UMAP embedding parameters a = 0.9922 b = 1.112
13:01:13 Read 5715 rows and found 10 numeric columns
13:01:13 Using Annoy for neighbor search, n_neighbors = 30
13:01:13 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:01:14 Writing NN index file to temp file /var/folders/ph/24prrxys02179y9_qzhxjgvc0000gn/T//Rtmpryniy5/fileb7bdf4fd43e
13:01:14 Searching Annoy index using 1 thread, search_k = 3000
13:01:15 Annoy recall = 100%
13:01:16 Commencing smooth kNN distance calibration using 1 thread
13:01:18 Initializing from normalized Laplacian + noise
13:01:18 Commencing optimization for 500 epochs, with 232812 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:01:27 Optimization finishedDimPlot(cistococl2, reduction = 'umap')
# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in
cistococl2_lin_clust_list <- list()
for (i in fivecell_cDNA$CistoCoCl2){
temp_df <- as.data.frame(table(Idents(cistococl2)[cistococl2$Lineage == i]))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistococl2_lin_clust_list[[i]] <- temp_df
}
# Need to do a random sampling of the same thing
cistococl2_lin_clust_rand_list <- list()
num_iter <- 1000
for(j in 1:num_iter){
set.seed(j)
cistococl2_lin_clust_rand_list[[j]] <- list()
for (i in fivecell_cDNA$CistoCoCl2){
num_cells <- length(cistococl2$Lineage[cistococl2$Lineage == i])
temp_df <- as.data.frame(table(sample(Idents(cistococl2), num_cells, replace = F)))
colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
cistococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
}
}Build dataframe of overall simulation results
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistococl2 <- mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistococl2 <- (mean_cistococl2-mean(means_cistococl2_sim))/sd(means_cistococl2_sim)
pval_mean_cistococl2 <- pnorm(z_mean_cistococl2, mean(means_cistococl2_sim), sd(means_cistococl2_sim), lower.tail = F)
# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistococl2 <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistococl2 <- (weighted_mean_cistococl2-mean(weighted_means_cistococl2_sim))/sd(weighted_means_cistococl2_sim)
pval_wmean_cistococl2 <- pnorm(z_wmean_cistococl2, mean(weighted_means_cistococl2_sim), sd(weighted_means_cistococl2_sim), lower.tail = F)
# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistococl2 <- c()
for (i in 1:length(means_cistococl2_sim)){
sim_maxes <- unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
ttest_pval_cistococl2 <- cbind(ttest_pval_cistococl2,t.test(x = unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}
# Save outputs
save(cistococl2_lin_clust_list, cistococl2_lin_clust_rand_list,z_mean_cistococl2,pval_mean_cistococl2, z_wmean_cistococl2, ttest_pval_cistococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
rm(cistococl2)---
title: "Lineage expression over time"
output: html_notebook
---

#Set working directory to appropriate folder for inputs and outputs on Google Drive
```{r, setup, include=FALSE}
#knitr::opts_knit$set(root.dir = '/Volumes/GoogleDrive/My Drive/Fasse_Shared/AJF_Drive_copy/Experiments/AJF009') # for aria's computer
knitr::opts_knit$set(root.dir = '/Volumes/GoogleDrive/.shortcut-targets-by-id/1zSqx3IzXMwt6clUjwyqmlOf4G1K53lvy/Fasse_Shared/AJF_Drive_copy/Experiments/AJF009') # for dylan's computer

#2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/ is additional path for outputs

```

#Initialize
```{r include = FALSE}
rm(list = ls())
library(dplyr)
library(Seurat)
library(ggplot2)
library(RColorBrewer)
library(ggpubr)

`%nin%` = Negate(`%in%`)
```

# Load data
```{r}
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/all_data_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Preprocess_GEX/second_timepoint_merged.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Filtering_cDNA/resistant_lineage_lists.RData')

load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cis_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/cocl2_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_final_lineages.RData')
load('2022_01_14_analysis_scripts/2022_05_27_analysis/Assign_dominant_barcodes/dabtram_both_times_final_lineages.RData')

```

#Plot cell cycle
```{r}

# view cell cycle scores and phase assignments
DimPlot(all_data, group.by = "Phase")
DimPlot(all_data)
FeaturePlot(object = all_data, reduction = 'umap', features = ("nFeature_RNA"), pt.size = 1, combine = T, order = TRUE)
DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))

```

# Markers within each first drug object to find subgroups (ie analogous to NGFR/EGFR)
```{r include = FALSE}
#load('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/all_data_markers.RData')
cis_markers <- FindAllMarkers(cis, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
cocl2_markers <- FindAllMarkers(cocl2, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
dabtram_markers <- FindAllMarkers(dabtram, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

all_data.markers <- FindAllMarkers(all_data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) 
save(all_data.markers, cis_markers, cocl2_markers, dabtram_markers, file = 'all_data_markers.RData')
```

# need to make Idents metadata object which says if the cells are included in the combined lins list, or if they were filtered
```{r}
#find lineages that are maintained at both dabtram timepoints
fivecell_cDNA$DabTramMaintained <- Reduce(intersect, list(fivecell_cDNA$DabTram, fivecell_cDNA$DabTramtoDabTram))

filtered_meta <- rep(0, length(names(all_data$Lineage)))

#specify which cells are in lineages that pass filtering for that condition
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% combined_lins_list$DabTram)] <- 'Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% combined_lins_list$DabTramtoDabTram)] <- 'Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% combined_lins_list$DabTramtoCoCl2)] <- 'Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% combined_lins_list$DabTramtoCis)] <- 'Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% combined_lins_list$CoCl2)] <- 'Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% combined_lins_list$CoCl2toDabTram)] <- 'Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% combined_lins_list$CoCl2toCoCl2)] <- 'Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% combined_lins_list$CoCl2toCis)] <- 'Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% combined_lins_list$Cis)] <- 'Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% combined_lins_list$CistoDabTram)] <- 'Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% combined_lins_list$CistoCoCl2)] <- 'Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% combined_lins_list$CistoCis)] <- 'Resistant to CistoCis'

#specify which cells are in lineages of more than 5 cells
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTram)] <- 'Large Resistant to DabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramtoDabTram)] <- 'Large Resistant to DabTramtoDabTram'
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCoCl2)] <- 'Large Resistant to DabTramtoCoCl2'
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %in% fivecell_cDNA$DabTramtoCis)] <- 'Large Resistant to DabTramtoCis'
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2)] <- 'Large Resistant to CoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %in% fivecell_cDNA$CoCl2toDabTram)] <- 'Large Resistant to CoCl2toDabTram'
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCoCl2)] <- 'Large Resistant to CoCl2toCoCl2'
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %in% fivecell_cDNA$CoCl2toCis)] <- 'Large Resistant to CoCl2toCis'
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %in% fivecell_cDNA$Cis)] <- 'Large Resistant to Cis'
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %in% fivecell_cDNA$CistoDabTram)] <- 'Large Resistant to CistoDabTram'
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %in% fivecell_cDNA$CistoCoCl2)] <- 'Large Resistant to CistoCoCl2'
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %in% fivecell_cDNA$CistoCis)] <- 'Large Resistant to CistoCis'

# filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTram'
# filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %in% fivecell_cDNA$DabTramMaintained)] <- 'Maintained Resistant to DabTramtoDabTram'

#specify which cells are in lineages that did not pass filtering
filtered_meta[which(all_data$OG_condition == "dabtram" & all_data$Lineage %nin% combined_lins_list$DabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtodabtram" & all_data$Lineage %nin% combined_lins_list$DabTramtoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtococl2" & all_data$Lineage %nin% combined_lins_list$DabTramtoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "dabtramtocis" & all_data$Lineage %nin% combined_lins_list$DabTramtoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2" & all_data$Lineage %nin% combined_lins_list$CoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2todabtram" & all_data$Lineage %nin% combined_lins_list$CoCl2toDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2tococl2" & all_data$Lineage %nin% combined_lins_list$CoCl2toCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cocl2tocis" & all_data$Lineage %nin% combined_lins_list$CoCl2toCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cis" & all_data$Lineage %nin% combined_lins_list$Cis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistodabtram" & all_data$Lineage %nin% combined_lins_list$CistoDabTram & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistococl2" & all_data$Lineage %nin% combined_lins_list$CistoCoCl2 & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 
filtered_meta[which(all_data$OG_condition == "cistocis" & all_data$Lineage %nin% combined_lins_list$CistoCis & all_data$Lineage %nin% c("No Barcode", "Still multiple"))] <- 'Filtered out' 

#specify which cells had zero or multiple barcodes
filtered_meta[which(all_data$Lineage %in% c("No Barcode", "Still multiple"))] <- 'No Barcode'

print(table(filtered_meta))

```

```{r}
all_data$Resistant_filtered <- filtered_meta
Idents(all_data) <- all_data$Resistant_filtered
```

```{r}
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_plot.pdf', height = 10, width = 20)
DimPlot(all_data, group.by = 'ident', cols = )
DimPlot(all_data, group.by = "Lineage") + theme(legend.position = 'none')
dev.off()

```

```{r}
pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/test_violin.pdf', height = 10, width = 30)

VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'NGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Maintained Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)

VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'NGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)
VlnPlot(all_data, features = 'EGFR', idents = 'Large Resistant to DabTramtoDabTram', group.by = "Lineage") + theme(legend.position = 'none') + geom_boxplot(fill = 'white', width = 0.1)

dev.off()
#VlnPlot(all_data, features = 'NGFR', idents = all_data$OG_condition == 'dabtram', group.by = "lineage")
```

#Looking into the one lineage that switches ngfr -> egfr
```{r}

switch_lin_dabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtram'])
switch_lin_dabtramtodabtram <- names(all_data$orig.ident[all_data$Lineage == "Lin171516" & all_data$OG_condition == 'dabtramtodabtram'])

DimPlot(all_data, group.by = "OG_condition", cols = c('dabtram' = '#623594', 'cocl2' = '#0F8241', 'cis' = '#C96D29', 'dabtramtodabtram' = '#561E59', 'dabtramtococl2' = '#A2248E', 'dabtramtocis' = '#9D85BE', 'cocl2todabtram' = '#10413B', 'cocl2tococl2' = '#6ABD45', 'cocl2tocis' = '#6DC49C', 'cistodabtram' = '#A23622', 'cistococl2' = '#F49129', 'cistocis' = '#FBD08C'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram), cols.highlight = c('red'))
DimPlot(all_data, cells.highlight = list(switch_lin_dabtram, switch_lin_dabtramtodabtram), cols.highlight = c('blue', 'red'))

switch_lin <- names(dabtram$orig.ident[dabtram$Lineage == "Lin171516"])
DimPlot(dabtram)
DimPlot(dabtram, cells.highlight = list(switch_lin))

#need to integrate lineages into just dabtram object and then plot off of this isntead?
#vlnplot(all_data, features = ngfr, idents = just the cells resistant to dabtram, group.by = lineage but i only want those included in combined_lins_list$dabtram ?

```

#from dabtram_both_times, force 2 clusters in umap, plot vs ngfr egfr, find markers of these 2
```{r}

dabtram_both_times_markers <- FindAllMarkers(dabtram_both_times, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
DimPlot(dabtram_both_times)
DimPlot(dabtram_both_times, group.by = 'OG_condition')
FeaturePlot(dabtram_both_times, features = c('NGFR', 'EGFR', 'nFeature_RNA'))
```

#Assign cluster assignments per lineage, find average score per lineage - make plots in order
```{r}
#average cell assignments per lineage in dabtram_maintained

#want to do a stacked barplot here-- copy paste code from condition clustering

#get lineage and cluster data from seurat object, switch cluster identifiers from 0,1 to -1,1 (egfr, ngfr)
clusters_per_lin <- data.frame (Cluster = as.numeric(as.character(dabtram_both_times$seurat_clusters)), Lineage = dabtram_both_times$Lineage, condition = dabtram_both_times$OG_condition)
clusters_per_lin$Cluster[clusters_per_lin$Cluster == 0] <- -1
clusters_per_lin_list <- list()

# Need to get percent values of EGFR for the lineage after the first treatment
for (i in fivecell_cDNA$DabTram){
  currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')
  Var1 <- c(-1,1)
  Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == -1), # EGFR
  sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtram')$Cluster == 1))
  clusters_per_lin_list[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
  clusters_per_lin_list[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list[[i]]$Var1)), clusters_per_lin_list[[i]]$Freq)
}

# Build a dataframe for plotting based on % EGFR or NGFR per lineage after first treatment
clusters_per_lin_df <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score')
for (i in names(clusters_per_lin_list)){
    clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
  clusters_per_lin_df <- rbind(clusters_per_lin_df, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]/sum(clusters_per_lin_list[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list[[i]]$Freq[clusters_per_lin_list[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
}

# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df <- clusters_per_lin_df[with(clusters_per_lin_df, order(-Score,Num_cells )),]
clusters_per_lin_df$Lineage <- factor(clusters_per_lin_df$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# make list object for after second treatment
clusters_per_lin_list_sec <- list()

# Need to get percent values of EGFR for the lineage after the second treatment
for (i in fivecell_cDNA$DabTram){
  currentlin <- filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')
  Var1 <- c(-1,1)
  Freq <- c(sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == -1), # EGFR
  sum(filter(clusters_per_lin, clusters_per_lin$Lineage == i & clusters_per_lin$condition == 'dabtramtodabtram')$Cluster == 1))
  clusters_per_lin_list_sec[[i]] <- data.frame('Var1' = Var1, 'Freq' = Freq)
  clusters_per_lin_list_sec[[i]]$Score <- weighted.mean(as.numeric(as.character(clusters_per_lin_list_sec[[i]]$Var1)), clusters_per_lin_list_sec[[i]]$Freq)
}

# Build a dataframe for plotting based on % EGFR or NGFR per lineage after second treatment
clusters_per_lin_df_sec <- data.frame(matrix(ncol = 5, nrow = 0))
colnames(clusters_per_lin_df_sec) <- c('Lineage', 'Percent_cells', 'Num_cells', 'Score', 'Cluster')
for (i in names(clusters_per_lin_list)){

  if (sum(clusters_per_lin_list_sec[[i]]$Freq) < 5){
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 1, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'Died')) # Add Lineage died row
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'NGFR')) # Add NGFR row
    clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = 0, 'Cluster' = 'EGFR')) # Add EGFR row
  }else{ # Actually calculate percentages since the lineage survived/is more than 5 cells
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = 0, 'Num_cells' = 0, 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'Died')) # Add Lineage died row
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == 1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'NGFR')) # Add NGFR row
      clusters_per_lin_df_sec <- rbind(clusters_per_lin_df_sec, data.frame('Lineage' = i, 'Percent_cells' = clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]/sum(clusters_per_lin_list_sec[[i]]$Freq), 'Num_cells' = sum(clusters_per_lin_list_sec[[i]]$Freq[clusters_per_lin_list_sec[[i]]$Var1 == -1]), 'Score' = clusters_per_lin_list_sec[[i]]$Score[1], 'Cluster' = 'EGFR')) # Add EGFR row
    }
}

# Reorder so that EGFR dominant lineages are plot first
clusters_per_lin_df_sec$Lineage <- factor(clusters_per_lin_df_sec$Lineage, levels = rev(unique(clusters_per_lin_df$Lineage)))

# Plot
p1 <- ggplot(clusters_per_lin_df, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#000000","#FFFFFF")) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
p2 <- ggplot(clusters_per_lin_df_sec, aes(y = Percent_cells, x = Lineage, fill = Cluster)) + geom_bar(stat = 'identity', position = position_fill(reverse = T), color = '#D3D3D3') + scale_fill_manual(values = c("#FF0000","#000000", '#FFFFFF')) + theme_classic() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

pdf('2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/stacked_bar_EGFR_NGFR_Died.pdf')
ggarrange(p1,p2, nrow = 2)
dev.off()
```

# Look at whether lineages cluster together in each individual condition - Starting with DabTram
```{r}
rm(dabtram, cis, cocl2) # Remove old seurat objects that are no longer needed
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtram
dabtram <- subset(all_data, idents = 'dabtram') # Subset down to the dabtram object

ElbowPlot(dabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtram <- FindNeighbors(dabtram, dims = 1:10)
dabtram <- FindClusters(dabtram, resolution = 0.5)
dabtram <- RunUMAP(dabtram, dims = 1:10)
DimPlot(dabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTram){
  temp_df <- as.data.frame(table(Idents(dabtram)[dabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtram_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  dabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTram){
    num_cells <- length(dabtram$Lineage[dabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the dabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtram <- mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtram <- (mean_dabtram-mean(means_dabtram_sim))/sd(means_dabtram_sim)
pval_mean_dabtram <- pnorm(z_mean_dabtram, mean(means_dabtram_sim), sd(means_dabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtram <- weighted.mean(unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtram_sim <- sapply(1:length(dabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtram <- (weighted_mean_dabtram-mean(weighted_means_dabtram_sim))/sd(weighted_means_dabtram_sim)
pval_wmean_dabtram <- pnorm(z_wmean_dabtram, mean(weighted_means_dabtram_sim), sd(weighted_means_dabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtram <- c()
for (i in 1:length(means_dabtram_sim)){
  sim_maxes <- unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtram <- cbind(ttest_pval_dabtram,t.test(x = unlist(lapply(dabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtram_lin_clust_list, dabtram_lin_clust_rand_list,z_mean_dabtram,pval_mean_dabtram, z_wmean_dabtram, ttest_pval_dabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtram_sim_results.RData')
rm(dabtram)
```

# Look at whether lineages cluster together in each individual condition -  DabTramtoDabTram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtodabtram
dabtramtodabtram <- subset(all_data, idents = 'dabtramtodabtram') # Subset down to the dabtramtodabtram object

ElbowPlot(dabtramtodabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtodabtram <- FindNeighbors(dabtramtodabtram, dims = 1:10)
dabtramtodabtram <- FindClusters(dabtramtodabtram, resolution = 0.5)
dabtramtodabtram <- RunUMAP(dabtramtodabtram, dims = 1:10)
DimPlot(dabtramtodabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtodabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoDabTram){
  temp_df <- as.data.frame(table(Idents(dabtramtodabtram)[dabtramtodabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtodabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtodabtram_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtodabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoDabTram){
    num_cells <- length(dabtramtodabtram$Lineage[dabtramtodabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtodabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```

# Significance testing of the dabtramtodabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtodabtram <- mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtodabtram <- (mean_dabtramtodabtram-mean(means_dabtramtodabtram_sim))/sd(means_dabtramtodabtram_sim)
pval_mean_dabtramtodabtram <- pnorm(z_mean_dabtramtodabtram, mean(means_dabtramtodabtram_sim), sd(means_dabtramtodabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtodabtram <- weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtodabtram_sim <- sapply(1:length(dabtramtodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtodabtram <- (weighted_mean_dabtramtodabtram-mean(weighted_means_dabtramtodabtram_sim))/sd(weighted_means_dabtramtodabtram_sim)
pval_wmean_dabtramtodabtram <- pnorm(z_wmean_dabtramtodabtram, mean(weighted_means_dabtramtodabtram_sim), sd(weighted_means_dabtramtodabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtodabtram <- c()
for (i in 1:length(means_dabtramtodabtram_sim)){
  sim_maxes <- unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtodabtram <- cbind(ttest_pval_dabtramtodabtram,t.test(x = unlist(lapply(dabtramtodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtodabtram_lin_clust_list, dabtramtodabtram_lin_clust_rand_list,z_mean_dabtramtodabtram,pval_mean_dabtramtodabtram, z_wmean_dabtramtodabtram, ttest_pval_dabtramtodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtodabtram_sim_results.RData')
rm(dabtramtodabtram)
```

# Look at whether lineages cluster together in each individual condition -  DabTramtoCis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtocis
dabtramtocis <- subset(all_data, idents = 'dabtramtocis') # Subset down to the dabtramtocis object

ElbowPlot(dabtramtocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtocis <- FindNeighbors(dabtramtocis, dims = 1:10)
dabtramtocis <- FindClusters(dabtramtocis, resolution = 0.5)
dabtramtocis <- RunUMAP(dabtramtocis, dims = 1:10)
DimPlot(dabtramtocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtocis_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoCis){
  temp_df <- as.data.frame(table(Idents(dabtramtocis)[dabtramtocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtocis_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoCis){
    num_cells <- length(dabtramtocis$Lineage[dabtramtocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```

# Significance testing of the dabtramtocis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtocis <- mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtocis <- (mean_dabtramtocis-mean(means_dabtramtocis_sim))/sd(means_dabtramtocis_sim)
pval_mean_dabtramtocis <- pnorm(z_mean_dabtramtocis, mean(means_dabtramtocis_sim), sd(means_dabtramtocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtocis <- weighted.mean(unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtocis_sim <- sapply(1:length(dabtramtocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtocis <- (weighted_mean_dabtramtocis-mean(weighted_means_dabtramtocis_sim))/sd(weighted_means_dabtramtocis_sim)
pval_wmean_dabtramtocis <- pnorm(z_wmean_dabtramtocis, mean(weighted_means_dabtramtocis_sim), sd(weighted_means_dabtramtocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtocis <- c()
for (i in 1:length(means_dabtramtocis_sim)){
  sim_maxes <- unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtocis <- cbind(ttest_pval_dabtramtocis,t.test(x = unlist(lapply(dabtramtocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtocis_lin_clust_list, dabtramtocis_lin_clust_rand_list,z_mean_dabtramtocis,pval_mean_dabtramtocis, z_wmean_dabtramtocis, ttest_pval_dabtramtocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtocis_sim_results.RData')
rm(dabtramtocis)
```

# Look at whether lineages cluster together in each individual condition -  DabTramtoCis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to dabtramtococl2
dabtramtococl2 <- subset(all_data, idents = 'dabtramtococl2') # Subset down to the dabtramtococl2 object

ElbowPlot(dabtramtococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
dabtramtococl2 <- FindNeighbors(dabtramtococl2, dims = 1:10)
dabtramtococl2 <- FindClusters(dabtramtococl2, resolution = 0.5)
dabtramtococl2 <- RunUMAP(dabtramtococl2, dims = 1:10)
DimPlot(dabtramtococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
dabtramtococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$DabTramtoCoCl2){
  temp_df <- as.data.frame(table(Idents(dabtramtococl2)[dabtramtococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  dabtramtococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
dabtramtococl2_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  dabtramtococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$DabTramtoCoCl2){
    num_cells <- length(dabtramtococl2$Lineage[dabtramtococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(dabtramtococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    dabtramtococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```

# Significance testing of the dabtramtococl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_dabtramtococl2 <- mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_dabtramtococl2 <- (mean_dabtramtococl2-mean(means_dabtramtococl2_sim))/sd(means_dabtramtococl2_sim)
pval_mean_dabtramtococl2 <- pnorm(z_mean_dabtramtococl2, mean(means_dabtramtococl2_sim), sd(means_dabtramtococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_dabtramtococl2 <- weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(dabtramtococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_dabtramtococl2_sim <- sapply(1:length(dabtramtococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(dabtramtococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_dabtramtococl2 <- (weighted_mean_dabtramtococl2-mean(weighted_means_dabtramtococl2_sim))/sd(weighted_means_dabtramtococl2_sim)
pval_wmean_dabtramtococl2 <- pnorm(z_wmean_dabtramtococl2, mean(weighted_means_dabtramtococl2_sim), sd(weighted_means_dabtramtococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_dabtramtococl2 <- c()
for (i in 1:length(means_dabtramtococl2_sim)){
  sim_maxes <- unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_dabtramtococl2 <- cbind(ttest_pval_dabtramtococl2,t.test(x = unlist(lapply(dabtramtococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(dabtramtococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(dabtramtococl2_lin_clust_list, dabtramtococl2_lin_clust_rand_list,z_mean_dabtramtococl2,pval_mean_dabtramtococl2, z_wmean_dabtramtococl2, ttest_pval_dabtramtococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/dabtramtococl2_sim_results.RData')
rm(dabtramtococl2)
```

# Look at whether lineages cluster together in each individual condition - Starting with DabTram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2
cocl2 <- subset(all_data, idents = 'cocl2') # Subset down to the cocl2 object

ElbowPlot(cocl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2 <- FindNeighbors(cocl2, dims = 1:10)
cocl2 <- FindClusters(cocl2, resolution = 0.5)
cocl2 <- RunUMAP(cocl2, dims = 1:10)
DimPlot(cocl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2){
  temp_df <- as.data.frame(table(Idents(cocl2)[cocl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  cocl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2){
    num_cells <- length(cocl2$Lineage[cocl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2 <- mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2 <- (mean_cocl2-mean(means_cocl2_sim))/sd(means_cocl2_sim)
pval_mean_cocl2 <- pnorm(z_mean_cocl2, mean(means_cocl2_sim), sd(means_cocl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2 <- weighted.mean(unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2_sim <- sapply(1:length(cocl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2 <- (weighted_mean_cocl2-mean(weighted_means_cocl2_sim))/sd(weighted_means_cocl2_sim)
pval_wmean_cocl2 <- pnorm(z_wmean_cocl2, mean(weighted_means_cocl2_sim), sd(weighted_means_cocl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2 <- c()
for (i in 1:length(means_cocl2_sim)){
  sim_maxes <- unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2 <- cbind(ttest_pval_cocl2,t.test(x = unlist(lapply(cocl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2_lin_clust_list, cocl2_lin_clust_rand_list,z_mean_cocl2,pval_mean_cocl2, z_wmean_cocl2, ttest_pval_cocl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2_sim_results.RData')
rm(cocl2)
```

# Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCoCl2
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tococl2
cocl2tococl2 <- subset(all_data, idents = 'cocl2tococl2') # Subset down to the cocl2tococl2 object

ElbowPlot(cocl2tococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2tococl2 <- FindNeighbors(cocl2tococl2, dims = 1:10)
cocl2tococl2 <- FindClusters(cocl2tococl2, resolution = 0.5)
cocl2tococl2 <- RunUMAP(cocl2tococl2, dims = 1:10)
DimPlot(cocl2tococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2tococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toCoCl2){
  temp_df <- as.data.frame(table(Idents(cocl2tococl2)[cocl2tococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2tococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2tococl2_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  cocl2tococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toCoCl2){
    num_cells <- length(cocl2tococl2$Lineage[cocl2tococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2tococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2tococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2tococl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tococl2 <- mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tococl2 <- (mean_cocl2tococl2-mean(means_cocl2tococl2_sim))/sd(means_cocl2tococl2_sim)
pval_mean_cocl2tococl2 <- pnorm(z_mean_cocl2tococl2, mean(means_cocl2tococl2_sim), sd(means_cocl2tococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tococl2 <- weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tococl2_sim <- sapply(1:length(cocl2tococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tococl2 <- (weighted_mean_cocl2tococl2-mean(weighted_means_cocl2tococl2_sim))/sd(weighted_means_cocl2tococl2_sim)
pval_wmean_cocl2tococl2 <- pnorm(z_wmean_cocl2tococl2, mean(weighted_means_cocl2tococl2_sim), sd(weighted_means_cocl2tococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tococl2 <- c()
for (i in 1:length(means_cocl2tococl2_sim)){
  sim_maxes <- unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2tococl2 <- cbind(ttest_pval_cocl2tococl2,t.test(x = unlist(lapply(cocl2tococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2tococl2_lin_clust_list, cocl2tococl2_lin_clust_rand_list,z_mean_cocl2tococl2,pval_mean_cocl2tococl2, z_wmean_cocl2tococl2, ttest_pval_cocl2tococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tococl2_sim_results.RData')
rm(cocl2tococl2)
```

# Look at whether lineages cluster together in each individual condition - Starting with CoCl2toCis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2tocis
cocl2tocis <- subset(all_data, idents = 'cocl2tocis') # Subset down to the cocl2tocis object

ElbowPlot(cocl2tocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2tocis <- FindNeighbors(cocl2tocis, dims = 1:10)
cocl2tocis <- FindClusters(cocl2tocis, resolution = 0.5)
cocl2tocis <- RunUMAP(cocl2tocis, dims = 1:10)
DimPlot(cocl2tocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2tocis_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toCis){
  temp_df <- as.data.frame(table(Idents(cocl2tocis)[cocl2tocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2tocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2tocis_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  cocl2tocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toCis){
    num_cells <- length(cocl2tocis$Lineage[cocl2tocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2tocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2tocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2tocis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2tocis <- mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2tocis <- (mean_cocl2tocis-mean(means_cocl2tocis_sim))/sd(means_cocl2tocis_sim)
pval_mean_cocl2tocis <- pnorm(z_mean_cocl2tocis, mean(means_cocl2tocis_sim), sd(means_cocl2tocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2tocis <- weighted.mean(unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2tocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2tocis_sim <- sapply(1:length(cocl2tocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2tocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2tocis <- (weighted_mean_cocl2tocis-mean(weighted_means_cocl2tocis_sim))/sd(weighted_means_cocl2tocis_sim)
pval_wmean_cocl2tocis <- pnorm(z_wmean_cocl2tocis, mean(weighted_means_cocl2tocis_sim), sd(weighted_means_cocl2tocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2tocis <- c()
for (i in 1:length(means_cocl2tocis_sim)){
  sim_maxes <- unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2tocis <- cbind(ttest_pval_cocl2tocis,t.test(x = unlist(lapply(cocl2tocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2tocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2tocis_lin_clust_list, cocl2tocis_lin_clust_rand_list,z_mean_cocl2tocis,pval_mean_cocl2tocis, z_wmean_cocl2tocis, ttest_pval_cocl2tocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2tocis_sim_results.RData')
rm(cocl2tocis)
```

# Look at whether lineages cluster together in each individual condition - Starting with CoCl2toDabTram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cocl2todabtram
cocl2todabtram <- subset(all_data, idents = 'cocl2todabtram') # Subset down to the cocl2todabtram object

ElbowPlot(cocl2todabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cocl2todabtram <- FindNeighbors(cocl2todabtram, dims = 1:10)
cocl2todabtram <- FindClusters(cocl2todabtram, resolution = 0.5)
cocl2todabtram <- RunUMAP(cocl2todabtram, dims = 1:10)
DimPlot(cocl2todabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cocl2todabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$CoCl2toDabTram){
  temp_df <- as.data.frame(table(Idents(cocl2todabtram)[cocl2todabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cocl2todabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cocl2todabtram_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  cocl2todabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CoCl2toDabTram){
    num_cells <- length(cocl2todabtram$Lineage[cocl2todabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cocl2todabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cocl2todabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cocl2todabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cocl2todabtram <- mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cocl2todabtram <- (mean_cocl2todabtram-mean(means_cocl2todabtram_sim))/sd(means_cocl2todabtram_sim)
pval_mean_cocl2todabtram <- pnorm(z_mean_cocl2todabtram, mean(means_cocl2todabtram_sim), sd(means_cocl2todabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cocl2todabtram <- weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cocl2todabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cocl2todabtram_sim <- sapply(1:length(cocl2todabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cocl2todabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cocl2todabtram <- (weighted_mean_cocl2todabtram-mean(weighted_means_cocl2todabtram_sim))/sd(weighted_means_cocl2todabtram_sim)
pval_wmean_cocl2todabtram <- pnorm(z_wmean_cocl2todabtram, mean(weighted_means_cocl2todabtram_sim), sd(weighted_means_cocl2todabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cocl2todabtram <- c()
for (i in 1:length(means_cocl2todabtram_sim)){
  sim_maxes <- unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cocl2todabtram <- cbind(ttest_pval_cocl2todabtram,t.test(x = unlist(lapply(cocl2todabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cocl2todabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cocl2todabtram_lin_clust_list, cocl2todabtram_lin_clust_rand_list,z_mean_cocl2todabtram,pval_mean_cocl2todabtram, z_wmean_cocl2todabtram, ttest_pval_cocl2todabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cocl2todabtram_sim_results.RData')
rm(cocl2todabtram)
```

# Look at whether lineages cluster together in each individual condition - Starting with Cis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cis
cis <- subset(all_data, idents = 'cis') # Subset down to the cis object

ElbowPlot(cis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cis <- FindNeighbors(cis, dims = 1:10)
cis <- FindClusters(cis, resolution = 0.5)
cis <- RunUMAP(cis, dims = 1:10)
DimPlot(cis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cis_lin_clust_list <- list()

for (i in fivecell_cDNA$Cis){
  temp_df <- as.data.frame(table(Idents(cis)[cis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cis_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  cis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$Cis){
    num_cells <- length(cis$Lineage[cis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cis <- mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cis <- (mean_cis-mean(means_cis_sim))/sd(means_cis_sim)
pval_mean_cis <- pnorm(z_mean_cis, mean(means_cis_sim), sd(means_cis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cis <- weighted.mean(unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cis_sim <- sapply(1:length(cis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cis <- (weighted_mean_cis-mean(weighted_means_cis_sim))/sd(weighted_means_cis_sim)
pval_wmean_cis <- pnorm(z_wmean_cis, mean(weighted_means_cis_sim), sd(weighted_means_cis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cis <- c()
for (i in 1:length(means_cis_sim)){
  sim_maxes <- unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cis <- cbind(ttest_pval_cis,t.test(x = unlist(lapply(cis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cis_lin_clust_list, cis_lin_clust_rand_list,z_mean_cis,pval_mean_cis, z_wmean_cis, ttest_pval_cis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cis_sim_results.RData')
rm(cis)
```


# Look at whether lineages cluster together in each individual condition - Starting with cistocis
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistocis
cistocis <- subset(all_data, idents = 'cistocis') # Subset down to the cistocis object

ElbowPlot(cistocis) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistocis <- FindNeighbors(cistocis, dims = 1:10)
cistocis <- FindClusters(cistocis, resolution = 0.5)
cistocis <- RunUMAP(cistocis, dims = 1:10)
DimPlot(cistocis, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistocis_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoCis){
  temp_df <- as.data.frame(table(Idents(cistocis)[cistocis$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistocis_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistocis_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  cistocis_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoCis){
    num_cells <- length(cistocis$Lineage[cistocis$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistocis), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistocis_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cistocis simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistocis <- mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistocis <- (mean_cistocis-mean(means_cistocis_sim))/sd(means_cistocis_sim)
pval_mean_cistocis <- pnorm(z_mean_cistocis, mean(means_cistocis_sim), sd(means_cistocis_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistocis <- weighted.mean(unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistocis_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistocis_sim <- sapply(1:length(cistocis_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistocis_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistocis <- (weighted_mean_cistocis-mean(weighted_means_cistocis_sim))/sd(weighted_means_cistocis_sim)
pval_wmean_cistocis <- pnorm(z_wmean_cistocis, mean(weighted_means_cistocis_sim), sd(weighted_means_cistocis_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistocis <- c()
for (i in 1:length(means_cistocis_sim)){
  sim_maxes <- unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistocis <- cbind(ttest_pval_cistocis,t.test(x = unlist(lapply(cistocis_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistocis_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistocis_lin_clust_list, cistocis_lin_clust_rand_list,z_mean_cistocis,pval_mean_cistocis, z_wmean_cistocis, ttest_pval_cistocis, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistocis_sim_results.RData')
rm(cistocis)
```


# Look at whether lineages cluster together in each individual condition - Starting with cistodabtram
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistodabtram
cistodabtram <- subset(all_data, idents = 'cistodabtram') # Subset down to the cistodabtram object

ElbowPlot(cistodabtram) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistodabtram <- FindNeighbors(cistodabtram, dims = 1:10)
cistodabtram <- FindClusters(cistodabtram, resolution = 0.5)
cistodabtram <- RunUMAP(cistodabtram, dims = 1:10)
DimPlot(cistodabtram, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistodabtram_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoDabTram){
  temp_df <- as.data.frame(table(Idents(cistodabtram)[cistodabtram$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistodabtram_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistodabtram_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  cistodabtram_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoDabTram){
    num_cells <- length(cistodabtram$Lineage[cistodabtram$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistodabtram), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistodabtram_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cistodabtram simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistodabtram <- mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistodabtram <- (mean_cistodabtram-mean(means_cistodabtram_sim))/sd(means_cistodabtram_sim)
pval_mean_cistodabtram <- pnorm(z_mean_cistodabtram, mean(means_cistodabtram_sim), sd(means_cistodabtram_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistodabtram <- weighted.mean(unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistodabtram_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistodabtram_sim <- sapply(1:length(cistodabtram_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistodabtram_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistodabtram <- (weighted_mean_cistodabtram-mean(weighted_means_cistodabtram_sim))/sd(weighted_means_cistodabtram_sim)
pval_wmean_cistodabtram <- pnorm(z_wmean_cistodabtram, mean(weighted_means_cistodabtram_sim), sd(weighted_means_cistodabtram_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistodabtram <- c()
for (i in 1:length(means_cistodabtram_sim)){
  sim_maxes <- unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistodabtram <- cbind(ttest_pval_cistodabtram,t.test(x = unlist(lapply(cistodabtram_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistodabtram_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistodabtram_lin_clust_list, cistodabtram_lin_clust_rand_list,z_mean_cistodabtram,pval_mean_cistodabtram, z_wmean_cistodabtram, ttest_pval_cistodabtram, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistodabtram_sim_results.RData')
rm(cistodabtram)
```

# Look at whether lineages cluster together in each individual condition - Starting with cistococl2
```{r}
Idents(all_data) <- all_data$OG_condition # Change the idents to the OG condition for subsetting to cistococl2
cistococl2 <- subset(all_data, idents = 'cistococl2') # Subset down to the cistococl2 object

ElbowPlot(cistococl2) # The standard deviation seems to really level off at 10

# Recluster with the appropriate number of dimensions
cistococl2 <- FindNeighbors(cistococl2, dims = 1:10)
cistococl2 <- FindClusters(cistococl2, resolution = 0.5)
cistococl2 <- RunUMAP(cistococl2, dims = 1:10)
DimPlot(cistococl2, reduction = 'umap')

# Build list of lineages with at least 5 cells in dab tram and check what seurat cluster the cells are in 
cistococl2_lin_clust_list <- list()

for (i in fivecell_cDNA$CistoCoCl2){
  temp_df <- as.data.frame(table(Idents(cistococl2)[cistococl2$Lineage == i]))
  colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
  temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
  cistococl2_lin_clust_list[[i]] <- temp_df
}

# Need to do a random sampling of the same thing 
cistococl2_lin_clust_rand_list <- list()
num_iter <- 1000 
for(j in 1:num_iter){
  set.seed(j)
  cistococl2_lin_clust_rand_list[[j]] <- list()
  for (i in fivecell_cDNA$CistoCoCl2){
    num_cells <- length(cistococl2$Lineage[cistococl2$Lineage == i])
    temp_df <- as.data.frame(table(sample(Idents(cistococl2), num_cells, replace = F)))
    colnames(temp_df) <- c('Seurat_clust', 'Num_cells')
    temp_df$Pcnt_cells <- temp_df$Num_cells/sum(temp_df$Num_cells)
    cistococl2_lin_clust_rand_list[[j]][[i]] <- temp_df
  }
}
```
# Significance testing of the cistococl2 simulation
```{r}
# Find the mean of the maximum percentage of cells in a single cluster per lineage in each simulation
mean_cistococl2 <- mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))))
means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells)))))
z_mean_cistococl2 <- (mean_cistococl2-mean(means_cistococl2_sim))/sd(means_cistococl2_sim)
pval_mean_cistococl2 <- pnorm(z_mean_cistococl2, mean(means_cistococl2_sim), sd(means_cistococl2_sim), lower.tail = F)

# Find the weighted mean of the maximum percentage of cells in a single cluster per lineage in each simulation
weighted_mean_cistococl2 <- weighted.mean(unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),
unlist(lapply(cistococl2_lin_clust_list, function(x) sum(x$Num_cells))))
weighted_means_cistococl2_sim <- sapply(1:length(cistococl2_lin_clust_rand_list), function(y)
  weighted.mean(unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) max(x$Pcnt_cells))),
                unlist(lapply(cistococl2_lin_clust_rand_list[[y]], function(x) sum(x$Num_cells)))))
z_wmean_cistococl2 <- (weighted_mean_cistococl2-mean(weighted_means_cistococl2_sim))/sd(weighted_means_cistococl2_sim)
pval_wmean_cistococl2 <- pnorm(z_wmean_cistococl2, mean(weighted_means_cistococl2_sim), sd(weighted_means_cistococl2_sim), lower.tail = F)

# Compare each individual distribution of maxes to the observed max by t test and track pval
ttest_pval_cistococl2 <- c()
for (i in 1:length(means_cistococl2_sim)){
  sim_maxes <- unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells)))
  ttest_pval_cistococl2 <- cbind(ttest_pval_cistococl2,t.test(x = unlist(lapply(cistococl2_lin_clust_list, function(x) max(x$Pcnt_cells))),y = unlist(lapply(cistococl2_lin_clust_rand_list[[i]], function(x) max(x$Pcnt_cells))), alternative = 'greater')$p.value)
}

# Save outputs
save(cistococl2_lin_clust_list, cistococl2_lin_clust_rand_list,z_mean_cistococl2,pval_mean_cistococl2, z_wmean_cistococl2, ttest_pval_cistococl2, file = '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/cistococl2_sim_results.RData')
rm(cistococl2)
```

# Build dataframe of overall simulation results
```{r}
sim_results <- rbind(
  dabtram <- data.frame("Mean_Zscore" = z_mean_dabtram, "Mean_pval" = pval_mean_dabtram, "Weighted_Mean_Zscore" = z_wmean_dabtram, "Weighted_Mean_pval" = pval_wmean_dabtram, ttest_sims_sig = sum(ttest_pval_dabtram <= 0.05)),
  dabtramtodabtram <- data.frame("Mean_Zscore" = z_mean_dabtramtodabtram, "Mean_pval" = pval_mean_dabtramtodabtram, "Weighted_Mean_Zscore" = z_wmean_dabtramtodabtram, "Weighted_Mean_pval" = pval_wmean_dabtramtodabtram, ttest_sims_sig = sum(ttest_pval_dabtramtodabtram <= 0.05)),
  dabtramtococl2 <- data.frame("Mean_Zscore" = z_mean_dabtramtococl2, "Mean_pval" = pval_mean_dabtramtococl2, "Weighted_Mean_Zscore" = z_wmean_dabtramtococl2, "Weighted_Mean_pval" = pval_wmean_dabtramtococl2, ttest_sims_sig = sum(ttest_pval_dabtramtococl2 <= 0.05)),
  dabtramtocis <- data.frame("Mean_Zscore" = z_mean_dabtramtocis, "Mean_pval" = pval_mean_dabtramtocis, "Weighted_Mean_Zscore" = z_wmean_dabtramtocis, "Weighted_Mean_pval" = pval_wmean_dabtramtocis, ttest_sims_sig = sum(ttest_pval_dabtramtocis <= 0.05)),
  cis <- data.frame("Mean_Zscore" = z_mean_cis, "Mean_pval" = pval_mean_cis, "Weighted_Mean_Zscore" = z_wmean_cis, "Weighted_Mean_pval" = pval_wmean_cis, ttest_sims_sig = sum(ttest_pval_cis <= 0.05)),
  cistodabtram <- data.frame("Mean_Zscore" = z_mean_cistodabtram, "Mean_pval" = pval_mean_cistodabtram, "Weighted_Mean_Zscore" = z_wmean_cistodabtram, "Weighted_Mean_pval" = pval_wmean_cistodabtram, ttest_sims_sig = sum(ttest_pval_cistodabtram <= 0.05)),
  cistococl2 <- data.frame("Mean_Zscore" = z_mean_cistococl2, "Mean_pval" = pval_mean_cistococl2, "Weighted_Mean_Zscore" = z_wmean_cistococl2, "Weighted_Mean_pval" = pval_wmean_cistococl2, ttest_sims_sig = sum(ttest_pval_cistococl2 <= 0.05)),
  cistocis <- data.frame("Mean_Zscore" = z_mean_cistocis, "Mean_pval" = pval_mean_cistocis, "Weighted_Mean_Zscore" = z_wmean_cistocis, "Weighted_Mean_pval" = pval_wmean_cistocis, ttest_sims_sig = sum(ttest_pval_cistocis <= 0.05)),
  cocl2 <- data.frame("Mean_Zscore" = z_mean_cocl2, "Mean_pval" = pval_mean_cocl2, "Weighted_Mean_Zscore" = z_wmean_cocl2, "Weighted_Mean_pval" = pval_wmean_cocl2, ttest_sims_sig = sum(ttest_pval_cocl2 <= 0.05)),
  cocl2todabtram <- data.frame("Mean_Zscore" = z_mean_cocl2todabtram, "Mean_pval" = pval_mean_cocl2todabtram, "Weighted_Mean_Zscore" = z_wmean_cocl2todabtram, "Weighted_Mean_pval" = pval_wmean_cocl2todabtram, ttest_sims_sig = sum(ttest_pval_cocl2todabtram <= 0.05)),
  cocl2tococl2 <- data.frame("Mean_Zscore" = z_mean_cocl2tococl2, "Mean_pval" = pval_mean_cocl2tococl2, "Weighted_Mean_Zscore" = z_wmean_cocl2tococl2, "Weighted_Mean_pval" = pval_wmean_cocl2tococl2, ttest_sims_sig = sum(ttest_pval_cocl2tococl2 <= 0.05)),
  cocl2tocis <- data.frame("Mean_Zscore" = z_mean_cocl2tocis, "Mean_pval" = pval_mean_cocl2tocis, "Weighted_Mean_Zscore" = z_wmean_cocl2tocis, "Weighted_Mean_pval" = pval_wmean_cocl2tocis, ttest_sims_sig = sum(ttest_pval_cocl2tocis <= 0.05))
)

rownames(sim_results) <- c("dabtram", "dabtramtodabtram", "dabtramtococl2", "dabtramtocis", "cis", "cistodabtram", "cistococl2", "cistocis", "cocl2", "cocl2todabtram", "cocl2tococl2", "cocl2tocis")

write.csv(sim_results, '2022_01_14_analysis_scripts/2022_05_27_analysis/Lineage_expression/sim_results.csv', row.names = T) 
```

